关键词: 周界入侵, 异物检测, 聚合特征, 转置残差结构, 多通道注意力

Abstract: To address the issue of perimeter intrusions impacting train safety in railway environments, and to overcome the limitations of low accuracy and efficiency in existing methods, a perimeter intrusion foreign object detection approach is proposed based on the YOLOv9 model. The proposed feature aggregation module reduces the network’s computational complexity by employing a compact architecture, thereby enhancing detection efficiency. A multi-channel attention mechanism with inverted residual is proposed by integrating the transposed residual structure with the designed multi-channel attention. This approach reduces the number of convolutional parameters, promotes extensive interaction of information across channels, captures key features of the detection target, enhances anomaly detection accuracy, and minimizes both false negatives and false positives. The modified auxiliary detection branch effectively extracts image feature information while reducing the model’s parameter size. Experimental results demonstrate that the proposed model achieves an mAP@0.5 of 93.5% and a recall rate of 89.2% on the railway perimeter foreign object dataset, outperforming the YOLOv9 model by 6.1 and 4.6 percentage points, respectively, while reducing the model’s parameter count by 54.5%. Compared to other mainstream models, the proposed model achieves superior performance across key evaluation metrics, including mAP@0.5, recall rate, false positive rate, and false negative rate. In summary, the proposed model outperforms other mainstream models and demonstrates strong performance in perimeter intrusion detection tasks.

Key words: perimeter intrusion, foreign object detection, compact features, inverted residual structure, multi-channel attention