Comparative test results.

To solve the problems of low detection accuracy, large model size and slow reasoning speed of existing potato quality detection models, this paper proposes LPD-YOLOv7-Tiny, a lightweight potato sprout and spoilage detection model based on YOLOv7-Tiny. The proposed model introduces MobileNetV3 small, BiFormer, SimAM, and the Focal-EIOU loss function. MobileNetV3 small greatly reduces the number of parameters and computational complexity of the model, BiFormer enhances the multi-scale feature fusion capability of the model, and the SimAM module effectively suppresses irrelevant information and strengthens local features. The Focal-EIOU loss function improves the model’s attention to difficult classification samples and enhances its bounding box regression capability. LPD-YOLOv7-Tiny achieves excellent detection performance on potatoes under complex background conditions: mAP is increased to 90.3%, the number of parameters is reduced to 5.8 MB, the number of computations is reduced to 10.1 G, and the inference speed is increased to 142.5 fps. Compared with mainstream detection models such as the YOLO Basic series, SSD and speed-RCNN, LPD-YOLOv7-Tiny achieves significantly improved performance in terms of detection accuracy, positioning capability and computational efficiency, indicating it has wide application potential in resource-constrained and high-precision scenarios.

针对现有马铃薯品质检测模型存在的检测精度偏低、模型体量庞大、推理速度缓慢等问题，本文提出一种基于YOLOv7-Tiny的轻量化马铃薯芽变与腐烂检测模型LPD-YOLOv7-Tiny。所提模型集成了MobileNetV3 small、BiFormer、SimAM模块以及Focal-EIOU损失函数：其中MobileNetV3 small可大幅降低模型参数量与计算复杂度，BiFormer增强了模型的多尺度特征融合能力，SimAM模块能够有效抑制无关信息、强化局部特征，Focal-EIOU损失函数则提升了模型对难分类样本的关注度并增强了边界框回归性能。LPD-YOLOv7-Tiny在复杂背景条件下的马铃薯检测任务中取得了优异的检测性能：平均精度均值（mAP, mean Average Precision）提升至90.3%，参数量压缩至5.8 MB，计算量降至10.1 G，推理速度提升至142.5 fps。与YOLO Basic系列、SSD以及speed-RCNN等主流检测模型相比，LPD-YOLOv7-Tiny在检测精度、定位能力与计算效率上均实现了显著提升，表明其在资源受限且高精度需求的场景中具备广阔的应用潜力。