Feature Extraction with Apparent to Semantic Channels for Object Detection

Abstract

This paper focuses on using traditional image processing algorithms with some apparent-to-semantic features to improve the detection accuracy. Based on the optimization of Faster R-CNN algorithm, a mainstream framework in current object detection scenario, the multi-channel features are achieved by combining traditional image semantic feature algorithms (like Integral Channel Feature (ICF), Histograms of Gradient (HOG), Local Binary Pattern (LBF), etc.) and advanced semantic feature algorithms (like segmentation, heatmap, etc.). In order to realize the joint training of the original image and the above feature extraction algorithms, a unique network for increasing the accuracy of object detection and minimizing system weight called Multi-Channel Feature Network (MCFN) is proposed. The function of MCFN is to provide a multi-channel interface, which is not limited to the RGB component of a single picture, nor to the number of input channels. The experimental result shows the relationship between the number of additional channels, performance of model and accuracy. Compared with the basic Faster R-CNN structure, this result is based on the case of two additional channels. And the universal Mean Average Precision (mAP) can be improved by 2%-3%. When the number of extra channels is increased, the accuracy will not increase linearly. In fact, system performance starts to fluctuate in a range after the number of additional channels reaches six.