MDB-YOLO: A Lightweight, Multi-Dimensional Bionic YOLO for Real-Time Detection of Incomplete Taro Peeling

The automation of quality control in the food processing industry is critical for enhancing production efficiency, ensuring product safety, and reducing operational costs. A significant challenge in this domain is the automated inspection of tuber peeling, where incomplete processing can leave undesirable residual fragments. This paper addresses the specific problem of detecting small, low-contrast, and densely clustered residual taro peel fragments in a real-time industrial setting. We propose Multi-Dimensional Bionic YOLO (MDB-YOLO), a novel, lightweight object detection model based on the YOLOv8 architecture, specifically optimized for this challenging task. MDB-YOLO integrates several key innovations to achieve a superior balance of accuracy and efficiency: a C2f_EMA module enhances multi-scale feature representation through an efficient attention mechanism; Dynamic Upsampling (DySample) improves the reconstruction of high-resolution details; Omni-Dimensional Dynamic Convolution (ODConv2d) provides adaptive kernel learning; a BiFPN_Concat2 module is used for feature fusion; the Wise-IoU (WIoU) loss function focuses training on difficult examples; and Soft-NMS improves recall in dense scenes. Evaluated on a custom-built Taro Peel Industrial Dataset (TPID), MDB-YOLO achieves a mean Average Precision (mAP@0.5) of 92.1% and a mAP@0.5:0.95 of 69.7%, outperforming the YOLOv8s baseline and other state-of-the-art models. With only 13.44 million parameters and a computational load of 28.4 GFLOPS, MDB-YOLO demonstrates its suitability for lightweight deployment on resource-constrained edge devices, offering a practical and effective solution for automated quality control by delivering top-tier accuracy at a fraction of the computational cost and inference time of its competitors.