Detection failure and ghost point scenarios

[ajay1606]

Hello, Would you please share some tips to overcome detection failure with the following scenarios?

Issues;

1. The method shows the detected line. though there is a line that does not exist.
2. Road sign marking (Arrow ) also detected as a line
3. Unable to detect line during turning

I currently am using a pre-trained model: condlane_r101_culane.pth

With the configuration is as follows:

net = dict(
    type='Detector',
)

backbone = dict(
    type='ResNetWrapper',
    resnet='resnet101',
    pretrained=True,
    replace_stride_with_dilation=[False, False, False],
    out_conv=False,
    in_channels=[64, 128, 256, 512]
)

ori_img_h = 2048
ori_img_w = 2448
bbox_h_start = 1024
crop_bbox = [0, bbox_h_start, ori_img_w, ori_img_h]
sample_y = range(ori_img_h, bbox_h_start, -8)

batch_size = 1

aggregator = dict(
    type='TransConvEncoderModule',
    in_dim=2048,
    attn_in_dims=[2048, 256],
    attn_out_dims=[256, 256],
    strides=[1, 1],
    ratios=[4, 4],
    pos_shape=(batch_size, 10, 25),
)

neck=dict(
    type='FPN',
    in_channels=[256, 512, 1024, 256],
    out_channels=64,
    num_outs=4,
    #trans_idx=-1,
)

loss_weights=dict(
        hm_weight=1,
        kps_weight=0.4,
        row_weight=1.,
        range_weight=1.,
    )

num_lane_classes=1
heads=dict(
    type='CondLaneHead',
    heads=dict(hm=num_lane_classes),
    in_channels=(64, ),
    num_classes=num_lane_classes,
    head_channels=64,
    head_layers=1,
    disable_coords=False,
    branch_in_channels=64,
    branch_channels=64,
    branch_out_channels=64,
    reg_branch_channels=64,
    branch_num_conv=1,
    hm_idx=2,
    mask_idx=0,
    compute_locations_pre=True,
    location_configs=dict(size=(batch_size, 1, 80, 200), device='cuda:0')
)

optimizer = dict(type='AdamW', lr=3e-4, betas=(0.9, 0.999), eps=1e-8)

epochs = 16
total_iter = (88880 // batch_size) * epochs
import math
scheduler = dict(
    type = 'MultiStepLR',
    milestones=[8, 14],
    gamma=0.1
)

seg_loss_weight = 1.0
eval_ep = 1
save_ep = 1

img_norm = dict(
    mean=[75.3, 76.6, 77.6],
    std=[50.5, 53.8, 54.3]
)

img_height = 320
img_width = 800
cut_height = 0

mask_down_scale = 4
hm_down_scale = 16
num_lane_classes = 1
line_width = 3
radius = 6
nms_thr = 4
img_scale = (800, 320)
mask_size = (1, 80, 200)

train_process = [
    dict(type='Alaug',
    transforms=[dict(type='Compose', params=dict(bboxes=False, keypoints=True, masks=False)),
    dict(
        type='Crop',
        x_min=crop_bbox[0],
        x_max=crop_bbox[2],
        y_min=crop_bbox[1],
        y_max=crop_bbox[3],
        p=1),
    dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),
    dict(
        type='OneOf',
        transforms=[
            dict(
                type='RGBShift',
                r_shift_limit=10,
                g_shift_limit=10,
                b_shift_limit=10,
                p=1.0),
            dict(
                type='HueSaturationValue',
                hue_shift_limit=(-10, 10),
                sat_shift_limit=(-15, 15),
                val_shift_limit=(-10, 10),
                p=1.0),
        ],
        p=0.7),
    dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
    dict(
        type='OneOf',
        transforms=[
            dict(type='Blur', blur_limit=3, p=1.0),
            dict(type='MedianBlur', blur_limit=3, p=1.0)
        ],
        p=0.2),
    dict(type='RandomBrightness', limit=0.2, p=0.6),
    dict(
        type='ShiftScaleRotate',
        shift_limit=0.1,
        scale_limit=(-0.2, 0.2),
        rotate_limit=10,
        border_mode=0,
        p=0.6),
    dict(
        type='RandomResizedCrop',
        height=img_scale[1],
        width=img_scale[0],
        scale=(0.8, 1.2),
        ratio=(1.7, 2.7),
        p=0.6),
    dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),]

    ),
    dict(type='CollectLane',
        down_scale=mask_down_scale,
        hm_down_scale=hm_down_scale,
        max_mask_sample=5,
        line_width=line_width,
        radius=radius,
        keys=['img', 'gt_hm'],
        meta_keys=[
            'gt_masks', 'mask_shape', 'hm_shape',
            'down_scale', 'hm_down_scale', 'gt_points'
        ]
    ),
    #dict(type='Resize', size=(img_width, img_height)),
    dict(type='Normalize', img_norm=img_norm),
    dict(type='ToTensor', keys=['img', 'gt_hm'], collect_keys=['img_metas']),
]

val_process = [
    dict(type='Alaug',
        transforms=[dict(type='Compose', params=dict(bboxes=False, keypoints=True, masks=False)),
            dict(type='Crop',
            x_min=crop_bbox[0],
            x_max=crop_bbox[2],
            y_min=crop_bbox[1],
            y_max=crop_bbox[3],
            p=1),
        dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1)]
    ),
    #dict(type='Resize', size=(img_width, img_height)),
    dict(type='Normalize', img_norm=img_norm),
    dict(type='ToTensor', keys=['img']),
]

dataset_path = './data/CULane'
dataset = dict(
    train=dict(
        type='CULane',
        data_root=dataset_path,
        split='train',
        processes=train_process,
    ),
    val=dict(
        type='CULane',
        data_root=dataset_path,
        split='test',
        processes=val_process,
    ),
    test=dict(
        type='CULane',
        data_root=dataset_path,
        split='test',
        processes=val_process,
    )
)

workers = 12
log_interval = 1000
lr_update_by_epoch=True

Appreciate any response.

Is there any that I can modify the confidence score?

Regards, Ajay