Pavement defect detection based on multiscale image fusion

Existing target detection algorithms perform well in common road scenarios, however, in real complex road environments, where a large number of targets and small targets are prevalent, it is still a challenging task for pavement defect detection methods to achieve the ideal state. For this reason, this paper proposes a pavement defect detection network based on multi-scale image fusion. The network uses multiple detection heads and an almost parameter-free TripletAttention module to reach the interaction between different dimensions. To cope with the high-density target detection problem, we propose a YOLOv7 network structure combined with LSK spatial attention module and introduce a new multi-scale selective fusion module, MSF, to extract image features at different scales. Finally, this paper incorporates the activation function Mish() to enhance the model's ability to recognize overlapping and occluded objects. Experiments conducted on the dataset UDTIRI show that the improved method in this paper improves the overall AP by 4.5% and mIoU by 4.6% relative to YOLOv7 target detection algorithm for baseline networks. The ablation experiments and visualization performance demonstrate the rationality and superiority of this paper's method.