基于ACP-YOLOv5s的土栖白蚁活动迹象识别  

Identification of soil-dwelling termites activity signs based on ACP-YOLOv5s

作　　者：王一非 卢伟平 袁涛 陈龙佳 张峰 吴鹏飞 黄求应[2] WANG Yifei;LU Weiping;YUAN Tao;CHEN Longjia;ZHANG Feng;WU Pengfei;HUANG Qiuying(College of Informatics,Huazhong Agricultural University,Wuhan 430070,China;Key Laboratory of Termite Control of Ministry of Water Resources,Huazhong Agricultural University,Wuhan 430070,China;Engineering Research Center of Intelligent Technology for Agriculture,Ministry of Education,Wuhan 430070,China;Hubei Provincial Department of Water Resources,Wuhan 430071,China)

机构地区：[1]华中农业大学信息学院,武汉430070 [2]华中农业大学水利部白蚁防治重点实验室,武汉430070 [3]农业智能技术教育部工程研究中心,武汉430070 [4]湖北省水利厅,武汉430071

出　　处：《农业工程学报》2025年第1期221-229,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：湖北省防汛抗旱专项(HBCX-FW-2023-005)。

摘　　要：土栖白蚁作为严重危害水利工程和园林树木的重要害虫,其活动迹象的及时识别对于实施有效的蚁害预警和控制措施至关重要。针对这一需求,该研究开发了一种改进的一阶段目标检测算法——ACP-YOLOv5s,该算法基于广泛应用的YOLOv5s模型,通过集成自适应颜色感知模块(ACP-Module)对其进行优化,以增强模型在复杂自然环境下的特征提取和颜色感知能力,特别是在处理颜色混淆问题时有效提升模型的稳定性和泛化性能。在模型的颈部结构中加入CARFE上采样模块,通过扩展感受野并重新组织特征信息,以提高模型对细节的捕捉能力,从而提升识别精度。试验验证结果表明,改进后的ACP-YOLOv5s模型在土栖白蚁活动迹象检测中显示出更高的精确率和平均精度均值。与Faster R-CNN、YOLOv5s、YOLOv5m、YOLOv8和YOLOv9相比,ACP-YOLOv5s模型的精确率为91.2%,分别提升了5.3、5.0、3.4、7.9和0.1个百分点,平均精度均值为92.3%,分别提升了6.7、2.9、1.4、2.2和0.4个百分点,表明ACP-YOLOv5s模型在提高模型对复杂环境适应性方面的有效性,有助于加强对土栖白蚁侵害的早期预警和精确控制,为水利工程和园林树木白蚁防治工作提供了强有力的技术支持。Soil-dwelling termites are highly destructive pests that pose significant threats to hydraulic engineering structures and garden trees.These pests are notorious for causing extensive damage.Their activities were often characterized by mud covering,mud tunnel,and swarming hole.Early and accurate identification of these activity signs is essential for implementing timely and effective termite damage early warning systems and control measures.However,detecting the activity signs of soildwelling termites is a challenging task,primarily due to the complexity of natural environments,diverse background interference,and the frequent difficulty in distinguishing termite activity signs from their surrounding environment,particularly in cases where the colors of the signs and the background blend together.To address these practical challenges,this study proposed an advanced one-stage object detection algorithm named ACP-YOLOv5s.This algorithm was an improved version of the widely used YOLOv5s model and had been specifically optimized to enhance feature extraction and color perception capabilities in complex natural environments.The core innovation lied in the integration of an adaptive color perception module(ACP-Module)along with a dynamically adjustable threshold learning mechanism.The ACP-Module intelligently analyzed the color distribution of input images and dynamically determined the optimal color threshold range.This allowed the model to automatically adjust its sensitivity to colors based on varying image contents.This mechanism effectively mitigated detection instability caused by color confusion,significantly enhancing the stability and generalization capabilities of the model.As a result,the model performed exceptionally well even in complex scenarios with high levels of environmental noise and interference.To further enhance detection accuracy,a CARFE upsampling module was incorporated into the neck structure of the model.This module played a pivotal role in expanding the receptive field and reorganizing feature

关 键 词：目标检测 土栖白蚁 活动迹象 智能识别 YOLO 

分 类 号：S126[农业科学—农业基础科学]