嵌入scSE模块的改进YOLOv4小目标检测算法

为解决目标检测任务中小目标检测精度低，错检、漏检率高等问题，提出一种 scSE-IYOLOv4 的改进 YOLOv4 的小目标检测算法。实验使用 VEDAI 小目标数据集，采用 K-means++算法对目标样本进行锚 定框优化，以提升算法精度。在 YOLOv4 算法的基础上，分别研究分析了 scSE 注意力模块嵌入至模型不同位 置以及在模型颈部增加 SPP 模块对算法检测性能带来的影响。实验证明，在 YOLOv4 模型的骨干网“Add”和 “concat”层后嵌入 scSE 注意力模块，以及在颈部增加 SPP 模块均能有效提升算法对小目标的检测精度，在 VEDAI 测试集上 mAP@0.5 均提升了 2.4%。根据 YOLOv4 算法模型骨干网和颈部改进的实验结果，提出 scSE-IYOLOv4 目标检测算法。实验证明 scSE-IYOLOv4 算法能显著提升小目标的检测精度，在 VEDAI 测试集 上 mAP@0.5 值较 YOLOv4 提升了 4.1%，在 PASCAL VOC 数据集上 mAP@0.5 提升了 2.2%。

Improved YOLOv4 small target detection algorithm withembedded scSE module

To tackle the problems of low accuracy, high error rate, and high missed rate for small targets in target detection tasks, an improved YOLOv4 small target detection algorithm named scSE-IYOLOv4 was proposed. The experiment employed the VEDAI small target dataset and the K-means++ algorithm to optimize the anchor frame of the target sample, thereby improving the accuracy of the algorithm. Based on the YOLOv4 algorithm, studies and analyses were respectively conducted concerning the effect of the scSE attention module embedded in the model’s different positions and that of the addition of the SPP module to the model’s neck on the algorithm’s detection performance. The experiments proved that embedding the scSE attention module after the “Add” and “concat” layers of the backbone network of the YOLOv4 algorithm model, and adding the SPP module to the model’s neck can enhance the algorithm’s detection accuracy for small targets. The results showed that the mAP@0.5 both increased by 2.4% on the VEDAI test set. Finally, the scSE-IYOLOv4 algorithm was proposed based on the above experimental results of the improved backbone network and neck of the YOLOv4 model. It was proved by the experiment that scSE-IYOLOv4 can significantly heighten the detection accuracy for small targets. The value of mAP@0.5 on the VEDAI test set increased by 4.1%, compared with YOLOv4, and rose by 2.2% on the PASCAL VOC dataset.