LC-BiDet: Laterally Connected Binary Detector With Efficient Image Processing

Recently, binary neural networks have received increasing interest in object detection community, since compared to the full-precision detection networks, they can reduce the memory and computation requirements significantly due to the efficient XNOR and BITCOUNT operations introduced by binarization. However, the final detection performance of binary detectors always suffers from a severe degradation compared with the full-precision counterparts. In this letter, to achieve a better trade-off between the inference efficiency and the detection performance, we propose to learn a laterally connected binary detector based on the multiple parallel binary subnets with a group sparse regularization term. Firstly, we simply adopt the parallel structure to improve the representation capacity of binary detectors. Secondly, we introduce the dense lateral connections between binary subnets in each convolutional layer to increase the information flows. Finally, to reduce the redundancy of the dense connections, we take the L21 norm as the regularization term to optimize the lateral connections in an end-to-end manner. Experimental results on PASCAL VOC and COCO datasets show that our proposed laterally connected binary detector could outperform the other state-of-the-art binary detectors.