Pavement Disease Detection Algorithm Focusing on Shape Features

Automatic pavement disease detection is a crucial technology for achieving intelligent road management. In addressing the challenges posed by small disease targets in pavement images, significant variations among different types of diseases, and complex background environments, an algorithm named FSF-YOLO(focusing on shape features YOLO) is proposed, which is based on the YOLOv8 architecture. This algorithm incorporates an enhanced feature extraction module designed to retain multi-dimensional spatial feature information, thereby enhancing the backbone network’s capability to extract features from low-resolution images and small disease targets. Additionally, it introduces a deformable attention feature fusion module that leverages the elongated shape features of diseases to expand the target recognition area and improve the model’s feature expression ability for long distance disease targets. Furthermore, the algorithm utilizes a grouped convolution space pyramid pool module to bolster the recognition of disease targets of varying sizes. Lastly, it employs lightweight shared convolutional detection heads to reduce both the number of network parameters and the computational load. Experimental results demonstrate that the proposed method offers superior performance in detecting various types of pavement diseases, with an average accuracy of 67.3% on the RDD2022 dataset, which is a 5.3 percentage points improvement over the original algorithm. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.