Lsf-rdd: a local sensing feature network for road damage detection.

As an essential object detection application, road damage detection aims to identify and mark road damage. Timely maintenance of detected damage can improve road safety. However, the proportions of damage area of the image is very diffident for the variety of the road damage textures and shapes. Additionally it is a challenge to localize the road damage accurately for the blurring of the damaged regions caused by the external environmental factors. In this study, we propose a Road Damage Detector with a Local Sensing Feature Network (LSF-RDD), which constructs a Local Sensing Feature Network (LSF-Net) as a neck to fuse multi-scale features extracted from the backbone network and can focus on the location of the damaged area. First, the CSP-Darknet53 backbone network extracts the feature maps of three scales layer-by-layer from the input images. Second, these three feature maps are input into LSF-Net for multi-scale feature fusion to generate three local feature representations. LSF-Net comprises four interconnected blocks, enabling top-down and bottom-up feature fusion. Feature maps from the backbone perform multi-scale feature fusion through connections between different blocks. Finally, three local feature representations are sent into the detection head for detection. Experiments show that LSF-RDD performs well on the adopted datasets, especially on the China_motorbike dataset of RDD2022, with mAP@0.5 reaching 94.4%. [ABSTRACT FROM AUTHOR]