GLE-YOLO: intelligent detection of road linear damage in shadow condition

GLE-YOLO: intelligent detection of road linear damage in shadow condition

연구 분야: Verification

학회: Signal, Image and Video Processing

Road damage detection is a crucial task of road inspection systems. Although traditional object detection models achieve promising performance, the presence of shadows exacerbates the difficulty of road damage detection in practical scenarios. To tackle these challenges, we introduce a novel shadow-image enhancement network named global–local enhancement network and joint it with the YOLOv7-tiny detection network augmented with components by us to craft an end-to-end detection framework. We integrate deep neural networks with conventional methods and propose the global statistical texture enhancement module to enhance global statistical texture information. We propose the local enhancement module to enhance road damage edge information in shadow regions. Furthermore, we craft a shadow region loss to optimize the enhancement models and employ dynamic snake convolution to replace certain traditional convolution in detection network. We evaluate our method on shadow linear road damage datasets, SRoad and DRoad, which comprise road images from different perspectives in Beijing, China. The results demonstrate that our approach surpasses the performance of low-light enhancement models and low-light detection models. The method achieves mAP of 71.2% and FPS of 98.8 on SRoad dataset while reaching mAP of 79.7% and FPS of 103.2 on DRoad dataset. The proposed model optimizes performance and model size, meeting the requirements for real-time processing in industrial applications.