Efficient SSD: A Real-Time Intrusion Object Detection Algorithm for Railway Surveillance
2020 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)(2020)
摘要
With the rapid development of the Chinese Railway, the research of lightweight and real-time railway perimeter intrusion detection algorithm is crucial for railway security. A single shot multibox detector (SSD) can detect intruding objects but cannot be deployed to the non-Graphics Processing Unit (GPU) equipment of the railway system to achieve real-time detection. In this paper, EfficientSSD, a real-time lightweight object detector is proposed for railway surveillance detection. Based on the conventional SSD model, EfficientSSD replaces VGG16 with EfficientNet-B3 as the backbone network. The detector was trained on the Pascal VOC dataset and then on a Railway dataset with the mAP of 76.6%. EfficientSSD runs at about 11 FPS on a CPU
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with an overall accuracy of 96.95%. Compared with the SSD, our method obtains a precision improvement of 4.8% with only 1/2x model size and 2x speed.
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关键词
railway security,intrusion detection,EfficientNet,SSD,lightweight neural network,transfer learning
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