【摘 要】
：
As one of the key technologies of intelligent vehicles,traffic sign detection is still a challenging task because of the tiny size of its target object.To address the challenge,we present a novel detection network improved from yolo-v3 for the tiny traffi
【机 构】
：
School of Computer Science&Engineering,Beihang University,Beijing 100191,China
论文部分内容阅读
As one of the key technologies of intelligent vehicles,traffic sign detection is still a challenging task because of the tiny size of its target object.To address the challenge,we present a novel detection network improved from yolo-v3 for the tiny traffic sign with high precision in real-time.First,a visual multi-scale attention module(MSAM),a light-weight yet effective module,is devised to fuse the multi-scale feature maps with channel weights and spatial masks.It increases the representation power of the network by emphasizing useful features and suppressing unnecessary ones.Second,we exploit effectively fine-grained features about tiny objects from the shallower layers through modifying backbone Darknet-53 and adding one prediction head to yolo-v3.Finally,a receptive field block is added into the neck of the network to broaden the receptive field.Experiments prove the effectiveness of our network in both quantitative and qualitative aspects.The mAP@0.5 of our network reaches 0.965 and its detection speed is 55.56 FPS for 512×512 images on the challenging Tsinghua-Tencent 100k(TT100k)dataset.
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