The detection of foreign object intrusion is crucial for ensuring the safety of railway operations. To address challenges such as low efficiency， suboptimal detection accuracy， and slow detection speed inherent in conventional comprehensive video monitoring systems for railways， a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies. In a bid to raise detection accuracy， the convolutional block attention module （CBAM）， including spatial and channel attention modules， is seamlessly integrated into the YOLOv5 model， giving rise to the CBAM-YOLOv5 model. Furthermore， the distance intersection-over-union_non-maximum suppression （DIoU_NMS） algorithm is employed in lieu of the weighted non-maximum suppression algorithm， resulting in improved detection performance for intrusive targets. To accelerate detection speed， the model undergoes pruning based on the batch normalization （BN） layer， and TensorRT inference acceleration techniques are employed， culminating in the successful deployment of the algorithm on edge devices. The CBAM-YOLOv5 model exhibits a notable 2.1% enhancement in detection accuracy when evaluated on a self-constructed railway dataset， achieving 95.0% for mean average precision （mAP）. Furthermore， the inference speed on edge devices attains a commendable 15 frame/s.