A Light-weighted SSD Network Design for Object Detection

 In the object detection method based on deep convolutional neural networks, the parameter amount of its model is often tens of megabytes, so it is difficult to deploy such a large model in edge devices such as mobile terminals with limited computing resources. In order to solve this problem, this paper combines lightweight network design and parameter quantification to achieve the lightweight of the network on the basis of one-stage object detector (SSD). First, we redesigned the SSD object detection framework based on ResNet50 and MobileNet and trained a model with full precision weights. Then, based on the full-precision weight model, a block-by-block quantization strategy is adopted to reduce the weight accuracy of the convolutional layer in the feature extraction layer to three values (zero, plus and minus one). The experimental results show that the joint scheme proposed in this paper can test 72.54% mAP on the Pascal VOC2007 dataset. Compared with other industry-leading lightweight object detection methods, the detection accuracy is higher and the model can occupy less memory space.