Abstract
Vehicle active safety technology (VAST) is the precondition of intelligent vehicle. VAST is useful to reduce traffic accidents, as well as to guarantee the personal and property security of the drivers. Vehicle detection plays as the foundation of VAST, however, it suffers from many challenges, e.g. partial occlusion, severe weather conditions, and perspective distortion. We employ the faster region convolution neural network (Faster RCNN) model in this work. We specialize it to vehicle detection through fine-tuning the model with vehicle samples. Full convolution layers and region proposal network are improved to enhance the detection performance of small-size vehicles. A small region up-sampling strategy is proposed to further improve the detection performance if the image is captured by a camera mounted on a vehicle. The effectiveness of the proposed approach is demonstrated through experiments in our own dataset and two benchmarking ones. Comparisons with baseline Faster RCNN indicate the superiority of our approach.
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Acknowledgment
This work has been supported by the National Natural Science Foundation of China under Grant No. 61501060, the Natural Science Foundation of Jiangsu Province under Grant No. BK20150271, Key Laboratory for New Technology Application of Road Conveyance of Jiangsu Province under Grant BM20082061708.
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Yang, B., Zhang, Y., Cao, J., Zou, L. (2018). On Road Vehicle Detection Using an Improved Faster RCNN Framework with Small-Size Region Up-Scaling Strategy. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_20
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DOI: https://doi.org/10.1007/978-3-319-92753-4_20
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