RIL-YOLO: a lightweight real-time object detection model on mobile devices for kart racing

The kart is a high-speed mechanical system that requires real-time and reliable visual perception, while motion blur, occlusion, and limited mobile computing resources pose significant challenges. To address these issues, we propose RIL-YOLO, a lightweight object detection framework based on YOLOv8 and optimized for mobile deployment. The method incorporates motion blur data augmentation, a re-parameterized shared convolutional detection head architecture, an inner-CIoU (complete intersection over union) loss, and LAMP (layer-adaptive sparsity for the magnitude-based pruning)-based pruning to improve robustness, localization accuracy, and inference efficiency. Experimental results show that, compared with YOLOv8n, RIL-YOLO improves mAP@0.5 and mAP@[0.5:0.95] by 2.8 % and 2.2 %, respectively, while reducing parameters by 83 %, lowering FLOPs (floating point operations per second) by 53 %, and increasing inference speed by approximately 25 %. The proposed method achieves a favorable balance between accuracy and real-time performance on resource-constrained mobile platforms.