This paper presents an enhanced YOLOv8 model designed to address multi-target detection challenges in complex traffic scenarios. The model integrates the Squeeze-and-Excitation attention mechanism, the deformable convolution C2f module, and the smooth IoU loss function, achieving significant improvements in detection accuracy and robustness in various complex environments. Experimental results show that the enhanced YOLOv8 model outperforms existing YOLO solutions across multiple metrics, particularly in precision and recall. Specifically, the enhanced model achieves 83.8% precision and 82.7% recall, improving 1.05 times in precision and 1.1 times in recall compared to the average precision (79.7%) and recall (75.4%) of other YOLO series models. In terms of mAP_0.5, the enhanced model achieves 89%, representing a 1.05-fold improvement over the average mAP_0.5 (84.4%) of YOLO series models. For mAP_0.5:0.95, the enhanced model reaches 76.5%, which is a 1.1-fold improvement over the average mAP_0.5:0.95 (69.7%) of YOLO series models. These improvements demonstrate the superior performance of the proposed model in multi-scale and complex scenarios, providing strong support for intelligent transportation systems and autonomous driving.

With the widespread adoption of electric vehicles, the power converter, as a key component, plays a crucial role. Traditional fault detection methods often face challenges in real-time performance and computational efficiency, making it difficult to meet the demands of electric vehicle power converters for efficient and accurate fault diagnosis. To address this challenge, this paper proposes a novel fault detection model—SpikingShuffleNet. This paper first designs an efficient SpikingShuffle Unit that integrates grouped convolutions and channel shuffle techniques, effectively reducing the model’s computational complexity by optimizing feature extraction and channel interaction. Next, by appropriately stacking SpikingShuffle Units and refining the network architecture, a complete lightweight diagnostic network is constructed for real-time fault detection in electric vehicle power converters. Finally, the Mixed Local Channel Attention mechanism is introduced to address the potential limitations in feature representation caused by grouped convolutions, further enhancing fault detection accuracy and robustness by balancing local detail preservation and global feature integration. Experimental results show that SpikingShuffleNet exhibits excellent accuracy and robustness in the fault detection task for power converters, fulfilling the real-time fault diagnosis requirements for low-power embedded devices.