中国农业科技导报2025，Vol.27Issue(6)：113-125,13.DOI:10.13304/j.nykjdb.2024.0493
融合亮度自适应模块的端到端低光环境黑猪检测技术研究
Research on End-to-end Low-light Environment Black Pig Detection Technology Integrating IAT Module
摘要
Abstract
To address the issues of poor image quality,difficulty in recognition and localization,as well as false positives and false negatives caused by occlusion and adhesion in scenarios involving clustered black pigs under low-light conditions,a detection model named low-light animal detection network(LADnet)was proposed.Firstly,an illumination-adaptive transformer(IAT)and a coordinate attention(CA)mechanism were utilized to enhance the brightness and reduce noise in the images.Then,a selective kernel convolutional attention(SKCA)module was designed to improve the model's ability to perceive black pigs.Finally,the ReLU activation function was employed to mitigate problems related to gradient vanishing and explosion.The results showed that the LADnet model achieved precision,recall and mean average precision(mAP@0.5)of 97.32%,86.61%and 92.73%,respectively,representing improvements of 1.07,6.15 and 3.05 percentage points compared to the baseline model.Compared to single-stage object detection models such as SSD and YOLOv5,LADnet achieved an average accuracy improvement of 8.33 and 7.35 percentage points,respectively.In comparison with two-stage models like Cascade R-CNN,Faster R-CNN and DAB_DETR,LADnet not only demonstrated higher detection accuracy but also achieved a smaller parameter size and faster detection speed,making it more suitable for the real-time detection requirements.The LADnet model demonstrated exceptional detection performance and enhanced robustness in low-light black pig detection tasks,providing an efficient and reliable tool for the accurate identification of black pigs in low-light environments,which holded significant importance for advancing the development of intelligent farming under low-light condition.关键词
黑猪盘点/目标检测/注意力机制/低光增强/特征提取/YOLOv7/智慧养殖Key words
black pig inventory/object detection/attention mechanism/low-light enhancement/feature extraction/YOLOv7/intelligent breeding分类
信息技术与安全科学引用本文复制引用
黄梦真,李皞,胡桓浚,李梓芃,盛钟尹,刘义凡,夏震言,郑奥运..融合亮度自适应模块的端到端低光环境黑猪检测技术研究[J].中国农业科技导报,2025,27(6):113-125,13.基金项目
湖北省教育厅科技计划项目(D20221604) （D20221604）
湖北省重点研发计划项目(2022BBA0018) （2022BBA0018）
湖北省科技人才服务企业项目(2023DJC109) （2023DJC109）
湖北省中央引导地方科技发展专项(2024EIA039). （2024EIA039）
