SSD Object Detection Algorithm Based on Feature Fusion and Channel Attention

International Journal of Advanced Network, Monitoring, and Controls

article

Leilei Fan¹  Jun Yu¹  Zhiyi Hu² 

[1] School of Computer Science and Engineering Xi’an Technological University Xi’an;Engineering Design Institute Army Research Loboratory Beijing

关键词: Object Detection;    Feature Fusion;    Attention Mechanism;    SSD Algorithm;   

DOI  :  10.2478/ijanmc-2022-0029

学科分类：社会科学、人文和艺术（综合）

来源: Asociación Regional De Diálisis Y Trasplantes Renales

【 摘 要 】

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.

【 授权许可】

CC BY-NC-ND