Parallel Multi-Branch Convolution Block Net for Fast and Accurate Object Detection

In order to maintain the high speed advantage of single-stage object detector and improve its detection accuracy, in this paper, we propose a parallel multi-branch convolution block, called PMCB, which can efficiently extract multi-scale object information at a specific layer to form a discriminative feature layer and boost the detection performance with little computational burden. Based on the PMCB module, we build PMCB Net on the basis of the single shot multibox detector (SSD) network by replacing the conventional convolution with PMCB at a specific layer. The performance of the proposed algorithm is compared with that of other state-of-the-art methods on PASCAL VOC2007, MS COCO test datasets. The experimental results show that the proposed algorithm greatly improved detection accuracy performance while only adding a negligible computational burden, which is very important for practical engineering applications.