SRS-YOLO: Improved YOLOv8-Based Smart Road Stud Detection

Smart road studs have been extensively deployed as road safety and data collection devices. Accurate and reliable detection of smart road studs and its further integration into the perception and control modules of connected and autonomous vehicles (CAVs) undoubtedly benefit road boundary detection, localization of CAVs and augument the safety of CAVs’ driving. This work investigates real-time, accurate and reliable detection of smart road studs, which is a challenging task for CAVs because existing methods fail to achieve accurate and real-time smart road stud detection, especially in harsh road environment. To address these challenges, we first build a real-world smart road stud dataset, and then propose and validate a lightweight and efficient smart road stud detection model based on the you only look once 8th version (YOLOv8), called SRS-YOLO. First, a Squeeze-and-Excitation (SE) attention module is used to improve the coarse-to-fine (C2F) module to differentiate the channel importance of feature maps and improve the detection accuracy of smart road studs. Second, a novel downsampling module (DownS) that integrates the average pooling and the max pooling is designed to reduce the number of parameters and minimize information loss during the downsampling process. Third, the loss function is replaced with the Normalized Wasserstein Distance (NWD) loss to alleviate the sensitivity to location deviations when computing the loss for small targets. The experimental results demonstrate that the proposed SRS-YOLO outperforms other state-of-the-art methods, and achieves a 87.92% mean average precision at a real-time speed of 78 frames/s. Our dataset is available at: https://github.com/wky-xidian/smart-road-stud-dataset.