Object Detection Based on the Improved Single Shot MultiBox Detector

Aiming at the poor effect of deep learning algorithm on small objects detection, the SSD object detection method based on feature fusion is proposed. The reasons for low detection rate and poor robustness of classical SSD object detection methods are analysed; and through the theoretical analysis and comparative experiments, the characteristic fusion layer was proposed. The shallow layers with high resolution and deep layers with strong semantics are fused with the feature fusion structure; finally, a complete feature fusion structure is designed with the residual block to increase the width and depth of the network. The contrast experiment on the PASCAL VOC dataset was conducted for detection capability and detection accuracy, and experimental result indicates that when the confidence is set to 0.5, the mAP of the SSD method based on feature fusion is 78.04%, which is 0.8% higher than the classical SSD algorithm and 4.8% higher than the Faster RCNN algorithm. Obviously, the proposed algorithm improves the ability of small objects, and verifies the effectiveness of the proposed algorithm.