Improved single shot detection using DenseNet for tiny target detection

作者：Wang, Shudi; Xu, Manman^*; Sun, Ying^*; Jiang, Guozhang^*; Weng, Yaoqing; Liu, Xin; Zhao, Guojun; Fan, Hanwen; Li, Jun; Zou, Cejing; Xie, Yuanmin; Huang, Li; Chen, Baojia

来源：CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35(2).

DOI：10.1002/cpe.7491

• dense connectivity
• DIoU-NMS
• small targets
• SSD
• target detection

As the development of deep learning and the continuous improvement of computing power, as well as the needs of social production, target detection has become a research hotspot in recent years. However, target detection algorithm has the problem that it is more sensitive to large targets and does not consider the feature-feature interrelationship, which leads to a high false detection or missed detection rate of small targets. An small target detection method (C-SSD) based on improved SSD is proposed, that replaces the backbone network VGG-16 of the SSD network with the improved dense convolution network (C-DenseNet) network to achieves further feature fusion through fast connections between dense blocks. The Introduction of residuals in the prediction layer and DIoU-NMS further improves the detection accuracy. Experimental results demonstrate that C-SSD outperforms other networks at three different image scales and achieves the best performance of 83. A 8% accuracy on the PASCAL VOC2007 test set, proving the effectiveness of the algorithm. C-SSD achieves a better balance of speed and accuracy, showing excellent performance in rapid detection of small targets.

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