The current vision-based aphid counting methods in water traps suffer from undercounts caused by occlusions and low visibility arising from dense aggregation of insects and other objects. To address this problem, we propose a novel aphid counting method through interactive stirring actions. We use interactive stirring to alter the distribution of aphids in the yellow water trap and capture a sequence of images which are then used for aphid detection and counting through an optimized small object detection network based on Yolov5. We also propose a counting confidence evaluation system to evaluate the confidence of count-ing results. The final counting result is a weighted sum of the counting results from all sequence images based on the counting confidence. Experimental results show that our proposed aphid detection network significantly outperforms the original Yolov5, with improvements of 33.9% in AP@0.5 and 26.9% in AP@[0.5:0.95] on the aphid test set. In addition, the aphid counting test results using our proposed counting confidence evaluation system show significant improvements over the static counting method, closely aligning with manual counting results.

翻译：当前基于视觉的水诱集器蚜虫计数方法因昆虫及其他物体密集聚集导致的遮挡和低可见度问题，普遍存在计数不足的缺陷。为解决此问题，我们提出一种通过交互式搅拌操作实现蚜虫计数的新方法。该方法利用交互式搅拌改变黄水诱集器中蚜虫的分布状态，采集连续图像序列，并通过基于Yolov5优化的细粒度目标检测网络实现蚜虫检测与计数。我们同时提出计数置信度评估体系，用于量化计数结果的可靠性。最终计数结果依据置信度对序列图像的所有计数结果进行加权融合。实验结果表明：我们提出的蚜虫检测网络性能显著优于原始Yolov5模型，在蚜虫测试集上AP@0.5提升33.9%，AP@[0.5:0.95]提升26.9%。此外，采用计数置信度评估体系的蚜虫计数测试结果较静态计数方法有显著改善，其计数精度与人工计数结果高度吻合。