KM-SSD Method for Vehicle and Pedestrian Detection

Abstract

Abstract: In view of the fact that the conventional improved SSD methods improve the object detection accuracy of SSD while reducing its detection speed, this paper proposes an improved KM-SSD method based on SSD. Firstly, K-means+〖KG-*3〗+ clustering algorithm is used to adaptively learn the ratio of width to height in prior boxes; Secondly, an efficient feature mergence module is designed to achieve high and low level feature information fusion; Finally, the KM-SSD method is verified on the challenging KITTI dataset. The experimental results show that the mAP of SSD is 62.7%, and its average detection time is 0.162 s; the mAP of KM-SSD is 69.8%, and its average detection time is 0.133 s. Consequently, KM-SSD method not only improves the accuracy of SSD in vehicle and pedestrian detection, but also improves the detection speed of SSD, which proves the effectiveness of K-means+〖KG-*3〗+ clustering algorithm and the efficiency of feature fusion method used in this paper.

Key words: SSD, vehicle and pedestrian detection, K-means+〖KG-*3〗+ clustering algorithm, mergence module

ZHENG Qin-hao , YANG Zhen , YANG Zhen . KM-SSD Method for Vehicle and Pedestrian Detection[J]. Computer and Modernization, 2021, 0(03): 51-56.

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