SSD Object Detection Algorithm Based on Feature Fusion and Channel Attention

Aiming at the problems of low object detection accuracy due to complex background and insufficient semantic information of shallow features in the object detection SSD algorithm, this paper improves the existing SSD algorithm. First, the original vgg16 network is replaced by the ResNet50 network, and the residual network structure as well as the Batch Normalization layer are added, which are used to improve the accuracy of the feature extraction network; Second, a feature fusion module is designed to fuse adjacent feature maps to improve the detection effect by integrating contextual information; Third, the SE attention mechanism is introduced to give channel weights adaptively and enhance the useful feature channels; Finally, the object detection analysis experiments are conducted on the PASCAL VOC2012 dataset. The experimental results show that the improved SSD algorithm in this paper is able to achieve an mean average precision of 72.7% in the data set, which is 2.1% better than the original SSD-VGG16 and greatly improves the object detection effect.