# Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions

arXiv: https://arxiv.org/abs/2411.10357 · HTML: https://mlsanity.com/paper/2411.10357/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [Contextual Transformer Networks for Visual Recognition](https://mlsanity.com/paper/2107.12292/) (2021)
- [Omni-Dimensional Dynamic Convolution](https://mlsanity.com/paper/2209.07947/) (2022)
- [You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery](https://mlsanity.com/paper/1805.09512/) (2018)
- [Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields](https://mlsanity.com/paper/2308.05257/) (2023)

## Built on by (most cited first, 2 of 2)

- [Counting with Confidence: Accurate Pest Monitoring in Water Traps](https://mlsanity.com/paper/2506.22438/) (2025)
- [Automated Pest Counting in Water Traps through Active Robotic Stirring for Occlusion Handling](https://mlsanity.com/paper/2510.21732/) (2025)

## Abstract (arXiv)

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.