Lightweight Object Detection Network Based on Convolutional Neural Network

Considering the high computational complexity and low detection speed of the common object detection algorithms on an embedded platform, this study proposes a lightweight object detection network (BENet) suitable for embedded platforms. First, the proposed network added a channel feature interweaving module to the MobileNetv2 lightweight network to design the backbone network, which effectively enhanced the feature expression of the lightweight backbone network. Second, an adaptive multiscale weighted feature fusion module was proposed to learn the correlation between the features with various scales by assigning weights to the features with different scales. Finally, we attempted to introduce a spatial pyramid pooling structure to obtain the context information of different receptive fields. The experimental results on the VOC dataset show that the proposed BENet maintains high object detection accuracy and speed while has lower computational complexity and smaller parameters. Additionally, it is more suitable for embedded platforms.