通过搅动水陷阱并结合图像序列计数，提升蚜虫检测精度。
Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions
- 用交互式搅动改变蚜虫分布，采集多帧图像进行检测。
- 相比原版Yolov5，[email protected]提升33.9%，AP@[0.5:0.95]提升26.9%。
- 基于置信度加权融合多帧结果，适合田间动态环境计数。
当前基于视觉的水陷阱蚜虫计数方法因昆虫密集聚集和遮挡导致漏检。为此，我们提出一种通过交互式搅动改善蚜虫分布的新方法。利用搅动改变蚜虫在黄色水陷阱中的位置，获取一系列图像，并通过优化的Yolov5小目标检测网络进行蚜虫检测与计数。同时提出计数置信度评估系统，根据各帧图像的置信度对结果进行加权求和。实验表明，所提检测网络在蚜虫测试集上，[email protected]提升33.9%，AP@[0.5:0.95]提升26.9%。采用置信度评估系统的计数结果显著优于静态计数法，接近人工计数结果。
原文摘要 · Abstract (English)
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 [email protected] 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.
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