Detecting potholes in asphalt pavement under small-sample conditions based on improved faster region-based convolution neural networks

Pavement potholes have low detection accuracy under the condition of small samples. To address this issue, we propose a method for efficient and accurate pothole detection under small-sample conditions based on an improved Faster R-CNN (Region-based Convolution Neural Networks). First, images consisting of different pothole shapes and sizes were acquired from different sources and then augmented and denoised to obtain an image set. Second, two representative target-detection models, Faster R-CNN and YOLOv3, were tested. The detection results indicate that Faster R-CNN achieves better detection performance. Furthermore, to overcome inconsistencies (missed detections and inaccurate position estimations), the feature extraction layers of VGG16, ZFNet, and ResNet50 networks were used in combination with Faster R-CNN. The results showed that the VGG16+Faster R-CNN fusion model yielded superior accuracy. Finally, the detection accuracy improved to 0.8997 after adjusting the size of the candidate frame, which also enabled successful detection of previously missed targets.