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학술대회 Performance Enhancement of YOLOv3 by Adding Prediction Layers with Spatial Pyramid Pooling for Vehicle Detection
Cited 28 time in scopus Download 11 time Share share facebook twitter linkedin kakaostory
김광주, 김병근, 정윤수, 최두현
International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2018, pp.411-416
18ZD1100, 대경권 지역산업 기반 ICT융합기술 고도화 지원사업, 문기영
In recent years, vision-based object detection methods using convolutional neural network (CNN) have been very successful. However, the object detection method using the CNN feature has a disadvantage that lots of feature maps should be generated in order to be robust against the scale change and the occlusion of the object. Also, simply raising a large number of feature maps does not improve performance. We propose a multi-scale vehicle detection with spatial pyramid pooling method which is robust to the scale change of the vehicle and the occlusion by improving the conventional YOLOv3 algorithm. The proposed method was evaluated through the UA-DETRAC benchmark and obtain the state-of-the-art mAP, which is better than those of the DPM, ACF, R-CNN, CompACT, NANO, SA-FRCNN, and Faster-RCNN2.
KSP 제안 키워드
CNN features, Convolution neural network(CNN), Detection Method, Feature Map, Multi-scale, Pooling method, R-CNN, Spatial Pyramid Pooling, Vehicle detection, performance enhancement, state-of-The-Art