# An Improved Lightweight Model for Defect Detection on Paths in Images
## 1 Introduction  
Against the backdrop of accelerating urbanization, a surge in sustainable transportation modes like cycling is placing unprecedented demands on road infrastructure quality and safety [1]. However, pavement defects such as cracks and potholes remain a prevalent and critical issue worldwide. These defects act as significant safety hazards, contributing to thousands of fatal accidents annually in countries like India, and incur substantial economic costs, with annual road maintenance expenditures in the United States alone surpassing USD 17 billion [2, 3, 4]. Therefore, developing efficient and accurate automated road defect detection technology is crucial for enhancing urban management and ensuring public safety. From human visual inspection to sophisticated automated and intelligent techniques, road fault identification technology has improved. The original manual visual inspection entirely relied on technicians walking along the roads, observing the road surface with the naked eye, and looking for cracks and other defects. Once cracks were found, technicians might use tools such as rulers to measure the width and length of the cracks and record data for further analysis. This traditional method still has some limitations, such as strong subjectivity, poor data consistency, and susceptibility to environmental and personnel experience influences. At the same time, when inspecting roads that allow high-speed driving, there is also a significant safety hazard for inspectors. Later, acoustic, displacement,pressure, and other sensors were introduced for auxiliary detection. However, data from different types of sensors may be difficult to integrate and compare, affecting comprehensive assessment and full automation. Moreover, high-quality sensors and detection equipment may be costly, which is not conducive to widespread application. Therefore, enhancing the efficiency of low-cost methods based on computer vision has become the preferred approach. Modern detection algorithms, driven by deep learning, have seen significant advancements, with research primarily focusing on two key directions: enhancing multi-scale feature representation and optimizing overall performance. To improve the detection of varied defect sizes, many approaches have incorporated sophisticated feature fusion modules, such as the PSA and LCFI modules in YPLNet [5] or the BiFPN concept [6]. Concurrently, other works have aimed to boost accuracy and efficiency by optimizing existing frameworks [7; 8], developing specialized networks for challenging conditions like noisy environments [9], or exploring advanced network backbones and attention mechanisms [10; 11]. However, while these methods yield performance improvements, they often introduce a significant computational burden or risk the loss of critical information during down-sampling. Consequently, a fundamental challenge in the current research lies in the trade-off between model lightweighting and detection accuracy. The field currently lacks an optimized solution that can effectively balance these two competing demands: significantly reducing computational cost while simultaneously preventing the loss of critical feature information, particularly when detecting small and varied road defects. To address this trade-off, this study builds upon the YOLOv8s framework. While the YOLO series continues to evolve with models like YOLOv10 [12], we selected YOLOv8s as our baseline due to its excellent and proven balance of performance, speed, and stability, which provides a mature foundation for lightweight optimizations [13]. This study introduces a new model called ACD-YOLOv8, composed of ADown, CCFM, DyHead, and YOLO [14] version 8 (YOLOv8s). The key contributions can be outlined as follows:  
1. This study incorporates the lightweight cross-scale feature fusion module (CCFM) from RT-DETR into YOLOv8s to improve detection performance for scale variations and small targets.