基于YOLO v5s和改进SORT算法的黑水虻幼虫计数方法

目前农业环境下的无序目标的精确计数有很高的应用需求，这种计数对其生物量、生物密度管理起到了重要的指导作用。如黑水虻幼虫目标追踪过程中，追踪对象具有高速和非线性的特征，常规算法存在追踪目标速度不足和丢失目标后的再识别困难等问题。针对以上问题，本文提出了一种改进SORT算法，通过改进卡尔曼滤波模型的方式提升目标追踪算法的快速性和准确性，提升了计数的精度。另外，针对黑水虻幼虫目标识别过程中幼虫性状的多样性和混料导致的复杂背景问题，本文通过实验对比多种深度学习网络性能选定YOLO v5s算法提取图像多维度特征，提升了目标识别精度。实验结果表明：在划线计数方面，本文提出的改进SORT算法与原模型相比，平均精度从91.36%提升到95.55%，提升4.19个百分点，通过仿真和实际应用，证明了本文模型的有效性；在目标识别方面，使用YOLO v5s模型在训练集上帧率为156f/s，mAP@0.5为99.10%，精度为90.11%，召回率为99.22%，综合性能优于其他网络。

Larvae of Black Soldier Fly Counting Based on YOLO v5s Network and Improved SORT Algorithm

There is a high application demand for accurate counting of disordered targets in agricultural environments, and such counting plays an important guiding role in their biomass and biological density management. In the process of larvae of black soldier fly target tracking, the tracking object has the characteristics of high speed and non-linearity, and the conventional algorithm has the problems of insufficient speed of tracking target and difficulty of re-identification after losing the target. To address these problems, an improved SORT algorithm was proposed, which improved the speed and accuracy of the target tracking algorithm by improving the Kalman filter model, and enhanced the counting accuracy. In addition, for the complex background problem caused by larval trait diversity and mixing in the process of black gadfly larval target recognition, the target recognition accuracy was improved by experimentally comparing the performance of multiple deep learning networks, which selected YOLO v5s algorithm to extract multidimensional features of images. The experimental results showed that in terms of delineation counting, the improved SORT algorithm improved the average accuracy by 4.19 percentage points compared with the original model, from 91.36% to 95.55%, and the effectiveness of the model was proved through simulation and practical application. In terms of target recognition, using the YOLO v5s model on the training set achieved a frame rate of 156f/s, mAP@0.5 value of 99.10%, accuracy of 90.11%, and recall rate of 99.22%. Its overall performance was better than other networks.