Feature extraction and segmentation of pavement distress using an improved hybrid task cascade network

An improved Hybrid Task Cascade (HTC) instance segmentation model was proposed to segment pavement surface distress intelligently and accurately. First, the automatic color equalization (ACE) algorithm was performed to preprocess the dataset. Then, HTC and the proposed HTC were described. The proposed HTC was modified using the ResNeXt101 network, balanced feature pyramid (BFP) and deformable convolution (DCN). The Soft non-maximum suppression method (Soft-NMS) and balanced L1 loss were used to supplement the above work. Finally, the average detection precision (AP) and the segmentation AP indexes were assessed by comparison and contrast. Experimental results demonstrated that the original HTC has crack miss detection and false detection problems. In contrast, the improved HTC has a recall rate as high as 96.1%, indicating a low miss-detection rate. Compared with other mainstream instance segmentation models, the improved HTC still leads in various indicators on our or public datasets. Comparisons between the predicted and true distress areas were analysed, the relative error of which was reasonable and satisfactory. The results help judge the degree of pavement distress and provide data support for repair and maintenance, which has essential engineering significance.