`segnet` Node Crashes with "double free or corruption" Error During Launch

When trying to launch the segnet node using the ros_deep_learning package with the following command:

roslaunch ros_deep_learning segnet.ros1.launch input:=v4l2:///dev/video4 output:=display://0

The node crashes with the following error:

...
[ INFO] [1724721245.224459610]: allocated CUDA memory for 1280x720 image conversion
double free or corruption (!prev)
[segnet-3] process has died [pid 176, exit code -6, cmd /ros_deep_learning/devel/lib/ros_deep_learning/segnet /segnet/image_in:=/video_source/raw __name:=segnet __log:=/root/.ros/log/a3c743d6-6411-11ef-87d9-28d0431cfa8d/segnet-3.log].
log file: /root/.ros/log/a3c743d6-6411-11ef-87d9-28d0431cfa8d/segnet-3*.log

Full Log Output:

... logging to /root/.ros/log/a3c743d6-6411-11ef-87d9-28d0431cfa8d/roslaunch-ubuntu-140.log
Checking log directory for disk usage. This may take a while.
Press Ctrl-C to interrupt
Done checking log file disk usage. Usage is <1GB.

started roslaunch server http://ubuntu:39177/

SUMMARY
========

PARAMETERS
 * /rosdistro: noetic
 * /rosversion: 1.16.0
 * /segnet/class_colors_path: 
 * /segnet/class_labels_path: 
 * /segnet/input_blob: 
 * /segnet/mask_filter: linear
 * /segnet/model_name: fcn-resnet18-mhp-...
 * /segnet/model_path: 
 * /segnet/output_blob: 
 * /segnet/overlay_alpha: 180.0
 * /segnet/overlay_filter: linear
 * /segnet/prototxt_path: 
 * /video_output/bitrate: 0
 * /video_output/codec: unknown
 * /video_output/resource: display://0
 * /video_source/height: 0
 * /video_source/loop: 0
 * /video_source/resource: v4l2:///dev/video4
 * /video_source/width: 0

NODES
  /
    segnet (ros_deep_learning/segnet)
    video_output (ros_deep_learning/video_output)
    video_source (ros_deep_learning/video_source)

auto-starting new master
process[master]: started with pid [153]
ROS_MASTER_URI=http://localhost:11311

setting /run_id to a3c743d6-6411-11ef-87d9-28d0431cfa8d
process[rosout-1]: started with pid [168]
started core service [/rosout]
process[video_source-2]: started with pid [175]
process[segnet-3]: started with pid [176]
process[video_output-4]: started with pid [177]
[ INFO] [1724721240.865774799]: opening video source: v4l2:///dev/video4
[gstreamer] initialized gstreamer, version 1.16.3.0
[gstreamer] gstCamera -- attempting to create device v4l2:///dev/video4
[ INFO] [1724721240.899158727]: opening video output: display://0
[OpenGL] glDisplay -- X screen 0 resolution:  1920x1080
[OpenGL] glDisplay -- X window resolution:    1920x1080
[gstreamer] gstCamera -- found v4l2 device: Intel(R) RealSense(TM) Depth Ca
[gstreamer] v4l2-proplist, device.path=(string)/dev/video4, udev-probed=(boolean)false, device.api=(string)v4l2, v4l2.device.driver=(string)uvcvideo, v4l2.device.card=(string)"Intel\(R\)\ RealSense\(TM\)\ Depth\ Ca", v4l2.device.bus_info=(string)usb-3610000.xhci-1.1, v4l2.device.version=(uint)330360, v4l2.device.capabilities=(uint)2225078273, v4l2.device.device_caps=(uint)69206017;
[gstreamer] gstCamera -- found 7 caps for v4l2 device /dev/video4
[gstreamer] [0] video/x-raw, format=(string)YUY2, width=(int)1280, height=(int)800, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 15/1, 10/1, 5/1 };
[gstreamer] [1] video/x-raw, format=(string)YUY2, width=(int)1280, height=(int)720, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 30/1, 15/1, 10/1, 5/1 };
[gstreamer] [2] video/x-raw, format=(string)YUY2, width=(int)848, height=(int)480, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 60/1, 30/1, 15/1, 5/1 };
[gstreamer] [3] video/x-raw, format=(string)YUY2, width=(int)640, height=(int)480, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 60/1, 30/1, 15/1, 5/1 };
[gstreamer] [4] video/x-raw, format=(string)YUY2, width=(int)640, height=(int)360, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 90/1, 60/1, 30/1, 15/1, 5/1 };
[gstreamer] [5] video/x-raw, format=(string)YUY2, width=(int)480, height=(int)270, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 90/1, 60/1, 30/1, 15/1, 5/1 };
[gstreamer] [6] video/x-raw, format=(string)YUY2, width=(int)424, height=(int)240, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction){ 90/1, 60/1, 30/1, 15/1, 5/1 };
[gstreamer] gstCamera -- selected device profile:  codec=raw format=yuyv width=1280 height=720
[gstreamer] gstCamera pipeline string:
[gstreamer] v4l2src device=/dev/video4 do-timestamp=true ! video/x-raw, format=(string)YUY2, width=(int)1280, height=(int)720 ! appsink name=mysink sync=false
[gstreamer] gstCamera successfully created device v4l2:///dev/video4
[video]  created gstCamera from v4l2:///dev/video4
------------------------------------------------
gstCamera video options:
------------------------------------------------
  -- URI: v4l2:///dev/video4
     - protocol:  v4l2
     - location:  /dev/video4
     - port:      4
  -- deviceType: default
  -- ioType:     input
  -- codec:      raw
  -- codecType:  cpu
  -- width:      1280
  -- height:     720
  -- frameRate:  30
  -- numBuffers: 4
  -- zeroCopy:   true
  -- flipMethod: none
  -- loop:       0
------------------------------------------------
[gstreamer] opening gstCamera for streaming, transitioning pipeline to GST_STATE_PLAYING
[gstreamer] gstreamer changed state from NULL to READY ==> mysink
[gstreamer] gstreamer changed state from NULL to READY ==> capsfilter0
[gstreamer] gstreamer changed state from NULL to READY ==> v4l2src0
[gstreamer] gstreamer changed state from NULL to READY ==> pipeline0
[gstreamer] gstreamer changed state from READY to PAUSED ==> capsfilter0
[gstreamer] gstreamer stream status CREATE ==> src
[gstreamer] gstreamer changed state from READY to PAUSED ==> v4l2src0
[gstreamer] gstreamer changed state from READY to PAUSED ==> pipeline0
[gstreamer] gstreamer message new-clock ==> pipeline0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> capsfilter0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> v4l2src0

segNet -- loading segmentation network model from:
       -- prototxt:   
       -- model:      networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx
       -- labels:     networks/FCN-ResNet18-MHP-512x320/classes.txt
       -- colors:     networks/FCN-ResNet18-MHP-512x320/colors.txt
       -- input_blob  'input_0'
       -- output_blob 'output_0'
       -- batch_size  1

[TRT]    TensorRT version 8.5.2
[TRT]    loading NVIDIA plugins...
[TRT]    Registered plugin creator - ::BatchedNMSDynamic_TRT version 1
[TRT]    Registered plugin creator - ::BatchedNMS_TRT version 1
[TRT]    Registered plugin creator - ::BatchTilePlugin_TRT version 1
[TRT]    Registered plugin creator - ::Clip_TRT version 1
[TRT]    Registered plugin creator - ::CoordConvAC version 1
[TRT]    Registered plugin creator - ::CropAndResizeDynamic version 1
[TRT]    Registered plugin creator - ::CropAndResize version 1
[TRT]    Registered plugin creator - ::DecodeBbox3DPlugin version 1
[TRT]    Registered plugin creator - ::DetectionLayer_TRT version 1
[TRT]    Registered plugin creator - ::EfficientNMS_Explicit_TF_TRT version 1
[TRT]    Registered plugin creator - ::EfficientNMS_Implicit_TF_TRT version 1
[TRT]    Registered plugin creator - ::EfficientNMS_ONNX_TRT version 1
[TRT]    Registered plugin creator - ::EfficientNMS_TRT version 1
[TRT]    Could not register plugin creator -  ::FlattenConcat_TRT version 1
[TRT]    Registered plugin creator - ::GenerateDetection_TRT version 1
[TRT]    Registered plugin creator - ::GridAnchor_TRT version 1
[TRT]    Registered plugin creator - ::GridAnchorRect_TRT version 1
[TRT]    Registered plugin creator - ::GroupNorm version 1
[TRT]    Registered plugin creator - ::InstanceNormalization_TRT version 1
[TRT]    Registered plugin creator - ::InstanceNormalization_TRT version 2
[TRT]    Registered plugin creator - ::LayerNorm version 1
[TRT]    Registered plugin creator - ::LReLU_TRT version 1
[TRT]    Registered plugin creator - ::MultilevelCropAndResize_TRT version 1
[TRT]    Registered plugin creator - ::MultilevelProposeROI_TRT version 1
[TRT]    Registered plugin creator - ::MultiscaleDeformableAttnPlugin_TRT version 1
[TRT]    Registered plugin creator - ::NMSDynamic_TRT version 1
[TRT]    Registered plugin creator - ::NMS_TRT version 1
[TRT]    Registered plugin creator - ::Normalize_TRT version 1
[TRT]    Registered plugin creator - ::PillarScatterPlugin version 1
[TRT]    Registered plugin creator - ::PriorBox_TRT version 1
[TRT]    Registered plugin creator - ::ProposalDynamic version 1
[TRT]    Registered plugin creator - ::ProposalLayer_TRT version 1
[TRT]    Registered plugin creator - ::Proposal version 1
[TRT]    Registered plugin creator - ::PyramidROIAlign_TRT version 1
[TRT]    Registered plugin creator - ::Region_TRT version 1
[TRT]    Registered plugin creator - ::Reorg_TRT version 1
[TRT]    Registered plugin creator - ::ResizeNearest_TRT version 1
[TRT]    Registered plugin creator - ::ROIAlign_TRT version 1
[TRT]    Registered plugin creator - ::RPROI_TRT version 1
[TRT]    Registered plugin creator - ::ScatterND version 1
[TRT]    Registered plugin creator - ::SeqLen2Spatial version 1
[TRT]    Registered plugin creator - ::SpecialSlice_TRT version 1
[TRT]    Registered plugin creator - ::SplitGeLU version 1
[TRT]    Registered plugin creator - ::Split version 1
[TRT]    Registered plugin creator - ::VoxelGeneratorPlugin version 1
[TRT]    detected model format - ONNX  (extension '.onnx')
[TRT]    desired precision specified for GPU: FASTEST
[TRT]    requested fasted precision for device GPU without providing valid calibrator, disabling INT8
[gstreamer] gstreamer stream status ENTER ==> src
[gstreamer] gstreamer message stream-start ==> pipeline0
[OpenGL] glDisplay -- display device initialized (1920x1080)
[video]  created glDisplay from display://0
------------------------------------------------
glDisplay video options:
------------------------------------------------
  -- URI: display://0
     - protocol:  display
     - location:  0
  -- deviceType: display
  -- ioType:     input
  -- width:      1920
  -- height:     1080
  -- frameRate:  0
  -- numBuffers: 4
  -- zeroCopy:   true
  -- flipMethod: none
  -- loop:       0
------------------------------------------------
[ INFO] [1724721241.062804675]: video_output node initialized, waiting for messages
[gstreamer] gstCamera -- onPreroll
[gstreamer] gstBufferManager recieve caps:  video/x-raw, format=(string)YUY2, width=(int)1280, height=(int)720, pixel-aspect-ratio=(fraction)1/1, framerate=(fraction)30/1, interlace-mode=(string)progressive
[gstreamer] gstBufferManager -- recieved first frame, codec=raw format=yuyv width=1280 height=720 size=1843200
[TRT]    [MemUsageChange] Init CUDA: CPU +222, GPU +0, now: CPU 259, GPU 3991 (MiB)
[TRT]    Trying to load shared library libnvinfer_builder_resource.so.8.5.2
[TRT]    Loaded shared library libnvinfer_builder_resource.so.8.5.2
[cuda]   allocated 4 ring buffers (1843200 bytes each, 7372800 bytes total)
[cuda]   allocated 4 ring buffers (8 bytes each, 32 bytes total)
[gstreamer] gstreamer changed state from READY to PAUSED ==> mysink
[gstreamer] gstreamer message async-done ==> pipeline0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> mysink
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> pipeline0
[cuda]   allocated 4 ring buffers (2764800 bytes each, 11059200 bytes total)
[ INFO] [1724721241.876176221]: allocated CUDA memory for 1280x720 image conversion
[gstreamer] gstreamer message qos ==> v4l2src0
[TRT]    [MemUsageChange] Init builder kernel library: CPU +302, GPU +390, now: CPU 583, GPU 4405 (MiB)
[TRT]    native precisions detected for GPU:  FP32, FP16, INT8
[TRT]    selecting fastest native precision for GPU:  FP16
[TRT]    found engine cache file /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx.1.1.8502.GPU.FP16.engine
[TRT]    found model checksum /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx.sha256sum
[TRT]    echo "$(cat /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx.sha256sum) /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx" | sha256sum --check --status
[TRT]    model matched checksum /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx.sha256sum
[TRT]    loading network plan from engine cache... /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx.1.1.8502.GPU.FP16.engine
[TRT]    device GPU, loaded /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx
[TRT]    Loaded engine size: 22 MiB
[TRT]    Deserialization required 27876 microseconds.
[TRT]    [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +22, now: CPU 0, GPU 22 (MiB)
[TRT]    Total per-runner device persistent memory is 1024
[TRT]    Total per-runner host persistent memory is 68096
[TRT]    Allocated activation device memory of size 7864320
[TRT]    [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +7, now: CPU 0, GPU 29 (MiB)
[TRT]    The getMaxBatchSize() function should not be used with an engine built from a network created with NetworkDefinitionCreationFlag::kEXPLICIT_BATCH flag. This function will always return 1.
[TRT]    
[TRT]    CUDA engine context initialized on device GPU:
[TRT]       -- layers       25
[TRT]       -- maxBatchSize 1
[TRT]       -- deviceMemory 7864320
[TRT]       -- bindings     2
[TRT]       binding 0
                -- index   0
                -- name    'input_0'
                -- type    FP32
                -- in/out  INPUT
                -- # dims  4
                -- dim #0  1
                -- dim #1  3
                -- dim #2  320
                -- dim #3  512
[TRT]       binding 1
                -- index   1
                -- name    'output_0'
                -- type    FP32
                -- in/out  OUTPUT
                -- # dims  4
                -- dim #0  1
                -- dim #1  21
                -- dim #2  10
                -- dim #3  16
[TRT]    
[TRT]    binding to input 0 input_0  binding index:  0
[TRT]    binding to input 0 input_0  dims (b=1 c=3 h=320 w=512) size=1966080
[TRT]    binding to output 0 output_0  binding index:  1
[TRT]    binding to output 0 output_0  dims (b=1 c=21 h=10 w=16) size=13440
[TRT]    
[TRT]    device GPU, /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx initialized.
[TRT]    segNet outputs -- s_w 16  s_h 10  s_c 21
[TRT]    loaded 21 class labels
[TRT]    class 00  color 0.000000 0.000000 0.000000 255.000000
[TRT]    class 01  color 139.000000 69.000000 19.000000 255.000000
[TRT]    class 02  color 222.000000 184.000000 135.000000 255.000000
[TRT]    class 03  color 210.000000 105.000000 30.000000 255.000000
[TRT]    class 04  color 255.000000 255.000000 0.000000 255.000000
[TRT]    class 05  color 255.000000 165.000000 0.000000 255.000000
[TRT]    class 06  color 0.000000 255.000000 0.000000 255.000000
[TRT]    class 07  color 60.000000 179.000000 113.000000 255.000000
[TRT]    class 08  color 107.000000 142.000000 35.000000 255.000000
[TRT]    class 09  color 255.000000 0.000000 0.000000 255.000000
[TRT]    class 10  color 245.000000 222.000000 179.000000 255.000000
[TRT]    class 11  color 0.000000 0.000000 255.000000 255.000000
[TRT]    class 12  color 0.000000 255.000000 255.000000 255.000000
[TRT]    class 13  color 238.000000 130.000000 238.000000 255.000000
[TRT]    class 14  color 128.000000 0.000000 128.000000 255.000000
[TRT]    class 15  color 255.000000 0.000000 0.000000 255.000000
[TRT]    class 16  color 255.000000 0.000000 255.000000 255.000000
[TRT]    class 17  color 128.000000 128.000000 128.000000 255.000000
[TRT]    class 18  color 192.000000 192.000000 192.000000 255.000000
[TRT]    class 19  color 128.000000 128.000000 128.000000 255.000000
[TRT]    class 20  color 128.000000 128.000000 128.000000 255.000000
[TRT]    loaded 21 class colors
[ INFO] [1724721244.917549168]: model hash => 1987947487477878254
[ INFO] [1724721244.920395247]: hash string => /usr/local/bin/networks/FCN-ResNet18-MHP-512x320/fcn_resnet18.onnx/usr/local/bin/networks/FCN-ResNet18-MHP-512x320/classes.txt
[ INFO] [1724721244.923732964]: node namespace => /segnet
[ INFO] [1724721244.923812487]: class labels => /segnet/class_labels_1987947487477878254
[ INFO] [1724721244.943880455]: segnet node initialized, waiting for messages
[ INFO] [1724721245.192920619]: allocated CUDA memory for 1280x720 image conversion
[ INFO] [1724721245.224459610]: allocated CUDA memory for 1280x720 image conversion
double free or corruption (!prev)
[segnet-3] process has died [pid 176, exit code -6, cmd /ros_deep_learning/devel/lib/ros_deep_learning/segnet /segnet/image_in:=/video_source/raw __name:=segnet __log:=/root/.ros/log/a3c743d6-6411-11ef-87d9-28d0431cfa8d/segnet-3.log].
log file: /root/.ros/log/a3c743d6-6411-11ef-87d9-28d0431cfa8d/segnet-3*.log

After running catkin_make clean and then catkin_make, I noticed some warnings about "control reaches end of non-void function." These warnings indicate that the functions publish_overlay, publish_mask_color, and publish_mask_class are expected to return a value, but the code paths leading to the end of these functions do not return anything. It also seems that imagenet and detectnet have similar warnings regarding their own publish_overlay functions.

root@ubuntu:/ros_deep_learning# catkin_make      
Base path: /ros_deep_learning
Source space: /ros_deep_learning/src
Build space: /ros_deep_learning/build
Devel space: /ros_deep_learning/devel
Install space: /ros_deep_learning/install
####
#### Running command: "make cmake_check_build_system" in "/ros_deep_learning/build"
####
####
#### Running command: "make -j6 -l6" in "/ros_deep_learning/build"
####
[  0%] Built target sensor_msgs_generate_messages_lisp
[  0%] Built target std_msgs_generate_messages_py
[  0%] Built target sensor_msgs_generate_messages_py
[  0%] Built target sensor_msgs_generate_messages_nodejs
[  0%] Built target sensor_msgs_generate_messages_cpp
[  0%] Built target sensor_msgs_generate_messages_eus
[  0%] Built target geometry_msgs_generate_messages_eus
[  0%] Built target geometry_msgs_generate_messages_lisp
[  0%] Built target geometry_msgs_generate_messages_nodejs
[  0%] Built target std_msgs_generate_messages_cpp
[  0%] Built target geometry_msgs_generate_messages_cpp
[  0%] Built target geometry_msgs_generate_messages_py
[  0%] Built target std_msgs_generate_messages_lisp
[  0%] Built target std_msgs_generate_messages_nodejs
[  0%] Built target std_msgs_generate_messages_eus
[  1%] Building CXX object ros_deep_learning/CMakeFiles/segnet.dir/src/node_segnet.cpp.o
[  3%] Building CXX object ros_deep_learning/CMakeFiles/detectnet.dir/src/node_detectnet.cpp.o
[  5%] Building CXX object ros_deep_learning/CMakeFiles/imagenet.dir/src/node_imagenet.cpp.o
[  6%] Building CXX object ros_deep_learning/CMakeFiles/video_source.dir/src/node_video_source.cpp.o
[  8%] Building CXX object ros_deep_learning/CMakeFiles/ros_deep_learning_nodelets.dir/src/nodelet_imagenet.cpp.o
[ 10%] Building CXX object ros_deep_learning/CMakeFiles/video_output.dir/src/node_video_output.cpp.o
/ros_deep_learning/src/ros_deep_learning/src/node_segnet.cpp: In function ‘bool publish_overlay(uint32_t, uint32_t)’:
/ros_deep_learning/src/ros_deep_learning/src/node_segnet.cpp:70:21: warning: control reaches end of non-void function [-Wreturn-type]
   70 |  sensor_msgs::Image msg;
      |                     ^~~
/ros_deep_learning/src/ros_deep_learning/src/node_segnet.cpp: In function ‘bool publish_mask_color(uint32_t, uint32_t)’:
/ros_deep_learning/src/ros_deep_learning/src/node_segnet.cpp:95:21: warning: control reaches end of non-void function [-Wreturn-type]
   95 |  sensor_msgs::Image msg;
      |                     ^~~
/ros_deep_learning/src/ros_deep_learning/src/node_segnet.cpp: In function ‘bool publish_mask_class(uint32_t, uint32_t)’:
/ros_deep_learning/src/ros_deep_learning/src/node_segnet.cpp:120:21: warning: control reaches end of non-void function [-Wreturn-type]
  120 |  sensor_msgs::Image msg;
      |                     ^~~
/ros_deep_learning/src/ros_deep_learning/src/node_imagenet.cpp: In function ‘bool publish_overlay(int, float)’:
/ros_deep_learning/src/ros_deep_learning/src/node_imagenet.cpp:82:21: warning: control reaches end of non-void function [-Wreturn-type]
   82 |  sensor_msgs::Image msg;
      |                     ^~~
[ 11%] Building CXX object ros_deep_learning/CMakeFiles/video_source.dir/src/image_converter.cpp.o
/ros_deep_learning/src/ros_deep_learning/src/node_detectnet.cpp: In function ‘bool publish_overlay(detectNet::Detection*, int)’:
/ros_deep_learning/src/ros_deep_learning/src/node_detectnet.cpp:72:21: warning: control reaches end of non-void function [-Wreturn-type]
   72 |  sensor_msgs::Image msg;
      |                     ^~~
[ 13%] Building CXX object ros_deep_learning/CMakeFiles/ros_deep_learning_nodelets.dir/src/image_converter.cpp.o
[ 15%] Building CXX object ros_deep_learning/CMakeFiles/video_output.dir/src/image_converter.cpp.o
[ 16%] Building CXX object ros_deep_learning/CMakeFiles/segnet.dir/src/image_converter.cpp.o
[ 18%] Building CXX object ros_deep_learning/CMakeFiles/imagenet.dir/src/image_converter.cpp.o
[ 20%] Building CXX object ros_deep_learning/CMakeFiles/detectnet.dir/src/image_converter.cpp.o
[ 22%] Building CXX object ros_deep_learning/CMakeFiles/video_source.dir/src/ros_compat.cpp.o
[ 23%] Linking CXX shared library /ros_deep_learning/devel/lib/libros_deep_learning_nodelets.so
[ 25%] Building CXX object ros_deep_learning/CMakeFiles/video_output.dir/src/ros_compat.cpp.o
[ 25%] Built target ros_deep_learning_nodelets
[ 25%] Built target _realsense2_camera_generate_messages_check_deps_DeviceInfo
[ 25%] Built target _realsense2_camera_generate_messages_check_deps_IMUInfo
[ 27%] Building CXX object ros_deep_learning/CMakeFiles/segnet.dir/src/ros_compat.cpp.o
[ 27%] Built target _realsense2_camera_generate_messages_check_deps_Extrinsics
[ 27%] Built target _realsense2_camera_generate_messages_check_deps_Metadata
[ 27%] Built target _catkin_empty_exported_target
[ 28%] Building CXX object ros_deep_learning/CMakeFiles/detectnet.dir/src/ros_compat.cpp.o
[ 30%] Building CXX object ros_deep_learning/CMakeFiles/imagenet.dir/src/ros_compat.cpp.o
[ 30%] Built target roscpp_generate_messages_cpp
[ 30%] Built target roscpp_generate_messages_eus
[ 30%] Built target roscpp_generate_messages_lisp
[ 32%] Linking CXX executable /ros_deep_learning/devel/lib/ros_deep_learning/video_source
[ 32%] Built target roscpp_generate_messages_nodejs
[ 32%] Built target roscpp_generate_messages_py
[ 32%] Built target rosgraph_msgs_generate_messages_cpp
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I suspect that these can lead to the memory corruption error.

Functions Identified:

publish_overlay(uint32_t, uint32_t)
publish_mask_color(uint32_t, uint32_t)
publish_mask_class(uint32_t, uint32_t)
publish_overlay(int, float)
publish_overlay(detectNet::Detection*, int)

How I fix the warnings

I updated all the affected functions by adding a return true; statement at the end of each function to ensure they return a value.

Concerns:

I am unsure if this solution might break the codebase in any way. However, after making these modifications, I can launch the nodes to start real-time inference without any issues.

dusty-nv / ros_deep_learning