Real-Time Pavement Damage Detection With Damage Shape Adaptation

Intelligent detection of pavement damage is crucial to road maintenance. Timely identification of cracks and potholes helps prolong the road service life. Current detection models fail to balance accuracy and speed. In this study, we propose a fast damage detection algorithm named FPDDN to achieve real-time and high-accuracy pavement damage detection. FPDDN integrates the deformable transformer, D2f block, and SFB module to predict pavement damage of different sizes in multiple branches. The deformable transformer allows the FPDDN to exhibit adaptability to geometric variations in road defects, thereby improving the detection accuracy of irregular defects such as cracks. D2f block is mainly used to lightweight the network and increase the inference speed. The SFB module can significantly decrease the loss of information during downsampling of small-sized objects. This integration enhances the model's ability to extract global damage features, reduces the loss of information on small-scale defects, and improves the synergy between deep and shallow feature layers. The model's performance was evaluated using the RDD2022 dataset, focusing on inference speed and detection accuracy. When compared to state-of-the-art models such as YOLO v8, FPDDN has a parameter count that is only one-fifth of that of YOLO v8x, yet it surpasses YOLO v8x in detection accuracy. The FPDDN achieved an F1 score of 0.601 and a mAP50 of 0.610 on the RDD2022 dataset, outperforming the compared models. Additionally, the algorithm achieved a balance between accuracy and speed with an inference speed of 1.8ms for pavement damage detection.