【摘 要】
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We present a practical approach to vehicle detection at container terminals based on a single camera and prevailing convolutional neural network models in computer vision domain.Aiming at container te
【机 构】
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Beihang University Beijing Trunk Technology Co.,L
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We present a practical approach to vehicle detection at container terminals based on a single camera and prevailing convolutional neural network models in computer vision domain.Aiming at container terminal scenarios,we introduce a specialized data labelling strategy for network training,as well as an optimized setting of crucial hyper-parameters,leading to a significant improvement on results.Our solution achieves 83#precision with 90#recall for semi-trailer trunks within 30 meters ahead of the vehicle-mounted monocular camera,at a speed of 32 frames per second(FPS)on a Nvidia Titan X for 416×416 image input,also providing more alternatives of fairly easy speed/accuracy trade-off.Compared to traditional lidar-based vehicle detection method for autonomous driving,our solution is more robust for particular container terminal scenarios while still maintaining a real-time performance by completely eliminating the region proposal stage.
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