GMS-YOLO: A Lightweight Real-Time Object Detection Algorithm for Pedestrians and Vehicles Under Foggy Conditions

In conditions of foggy weather, challenges, such as low light, blurred imagery, and dense fog that obscures target objects are prevalent. Moreover, computing resources are limited on edge devices. To tackle these challenges, a novel real-time detection algorithm GMS-YOLO for pedestrians and vehicles is proposed based on YOLOv10, which overcomes the semantic bottleneck of the model and enhances its detection performance. A novel ghost multiscale convolution (GMSConv) module is constructed, serving as the ghost multiScale feature extraction backbone network (GMS-Net). The Shape Consistent Intersection over Union (SCIoU) is introduced as the localization loss function, which takes into account the influence of the attributes of the regression box in the loss computation. Additionally, a compensatory consistency matching metric (CCMM) formula is designed to reduce the sensitivity of the original metric to IoU and regression scores. The GMS-YOLO algorithm has a lightweight structure, achieving FPS of 94 and 92 during the detection phase at the "'n"' and "'s"' sizes, respectively. Furthermore, we have deployed the model on Jetson Nano hardware, and the inference speed is also quite encouraging. We validated the effectiveness of the algorithm on the Foggy Cityscapes, RTTS, VOC2007-fog, and VOC2012-fog datasets. Experimental results indicate that GMS-YOLO outperforms the baseline model, with a mean average precision (mAP) improvement of 6.3% and 5.5% for the "n" and "s" scales, respectively. Consequently, the proposed GMS-YOLO algorithm not only demonstrates superior detection performance but also maintains a relatively low model complexity, significantly enhancing the efficiency and accuracy of object detection tasks in foggy environments. The source code for our algorithm is available at: https://github.com/Fwdchina/GMS-YOLO.