A Lightweight Object Detection Framework

Previous generic object detection networks depend on complex structures and huge computational costs, which are not conducive to deployment and application in resource-constrained scenarios, especially in mobile devices. Accordingly, exploring lightweight network has become a focus issue, attracting the widespread attention of industry and academia. In this paper, we proposed a lightweight two-stage object detection framework based convolutional neural network, which includes two components: the feature extractor and the detection components. For the feature extractor component, two architecture modules were proposed: residual cross-branch feature extraction module (RCM) and multi-scale feature fusion module (MFM). The RCM module adopted residual structure and the mechanism of cross-branch multi-channel to increase the receptive field and expression capability for low-level features. The MFM module integrated feature maps with multiple scales to enhance global context information. Moreover, for further optimizing the distribution of features and paying more attention to the detection-related regions, a separate channel self-attention module was proposed. Extensive experiments on the public datasets PASCAL VOC illustrated the superiority of the proposed model.