项目针对传统水果检测方法效率低、准确率不足的问题，提出了一种基于改进版YOLOv8s的轻量化高精度水果图像分类系统。研究首先整合多源数据集构建了包含17类水果的15,268张图像数据集，通过数据增强和分层抽样优化数据分布；随后在算法层面创新性地引入CoordAtt坐标注意力机制强化空间特征表达能力，采用AKConv自适应核卷积提升不规则目标识别鲁棒性，并优化SPPFCSPC模块实现高效多尺度特征融合，最终模型在测试集上达到89.99%的mAP@0.5，较基准提升3.75个百分点且参数量减少29.17%。研究进一步开发了基于PyQt5的跨平台可视化系统，支持图像/视频/摄像头多模态输 入，实现83.69FPS的实时检测与结构化数据输出功能，为智慧农业场景提供了实用的技术解决方案。
☆20Aug 5, 2025Updated last year
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