An Improved SSD Object Detection Algorithm Based on Attention Mechanism and Feature Fusion

Abstract

The Single Shot MultiBox Detector (SSD) is a well-known object detection method, but its detection of small objects is not effective. This paper makes modifications to the SSD object detection method to address its insufficient semantic information in low-level feature maps, thus enhancing the detectability for small objects. First, the Feature Pyramid Network (FPN) is incorporated into the SSD so that the shallow feature map, which is primarily utilized for detecting small objects, contains more semantic information in addition to rich location information. Second, the Convolutional Block Attention Module (CBAM) is introduced to reinforce the SSD network's capability to learn key features and thus improve missed detections. The experimental data indicate that this algorithm achieves 78.1% mAP in the PASCAL VOC2007test, a 3.9% improvement compared with the conventional SSD, and also has a great improvement compared with the Fast R-CNN and Faster R-CNN. As well as,this algorithm is better for small object detection and also meets the real-time requirements.