基于特征融合的SSD视觉小目标检测

针对SSD算法在检测目标过程中对小目标检测效果差的缺陷，提出了特征融合的SSD方法。该方法充分融合深浅层特征信息以提升网络模型对小目标的检测能力，为更好地检测小目标，将先验框尺寸相对原图比列进行调整，同时对SSD模型相应超参数值进行调整。实验结果表明，检测精度mAP较SSD提高3.4个百分点，对小目标Bottle、Chair、Plant检测精度分别提升8.7个百分点、3.4个百分点和7.1个百分点。检测精度mAP较当前一系列性能优异的目标检测算法有显著提高。通过拓展实验进一步证明改进算法成功检测到了大多数SSD算法没有检测到的小目标，提高了平均检测准确率。

SSD Visual Small Target Detection Based on Feature Fusion

Aiming at the shortcoming that SSD algorithm has poor detection effect on small targets, an improved SSD method is proposed based on feature fusion. The SSD method of feature fusion is proposed. This method fully integrates the deep and shallow feature information to improve the network model’s ability to detect small targets. In order to better detect small targets, this paper adjusts the prior frame size relative to the original image ratio column. At the same time, the corresponding hyperparameter values of the SSD model are adjusted. The experimental results show that the detection accuracy mAP is 3.4 percentage points higher than that of SSD, and the detection accuracy of small target Bottle, Chair and Plant is increased by 8.7, 3.4 and 7.1 percentage points, respectively. The detection accuracy mAP is significantly improved compared to the state-of-the-art target detection algorithms. In addition, the extended experiments show that the proposed algorithm can successfully detect the small targets that were not detected by traditional SSD, and improve the average detection accuracy.