Real-Time Rice Milling Morphology Detection Using Hybrid Framework of YOLOv8 Instance Segmentation and Oriented Bounding Boxes

Computer vision and image processing techniques have had great success in the food and drink industry. These technologies are used to analyse images, convert images to greyscale, and extract high-dimensional numerical data from the images; however, when it comes to real-time grain and rice milling processes, this technology has several limitations compared to other applications. Currently, milled rice image samples are collected and separated to avoid one contacting the another during analysis. This approach is not suitable for real-time industrial implementation. However, real-time analysis can be accomplished by utilising artificial intelligence (AI) and machine learning (ML) approaches instead of traditional quality assessment methods, such as manual inspection, which are labour-intensive, time-consuming, and prone to human error. To address these challenges, this paper presents a novel approach for real-time rice morphology analysis during milling by integrating You Only Look Once version 8 (YOLOv8) instance segmentation and Oriented Bounding Box (OBB) detection models. While instance segmentation excels in detecting and classifying both touching and overlapping grains, it underperforms in precise size estimation. Conversely, the object-oriented bounding box detection model provides more accurate size measurements but struggles with touching and overlapping grains. Experiments demonstrate that the hybrid system resolves key limitations of standalone models: instance segmentation alone achieves high detection accuracy (92% mAP@0.5) but struggles with size errors (0.35 mm MAE), while OBB alone reduces the size error to 0.12 mm MAE but falters with complex grain arrangements (88% mAP@0.5). By combining these approaches, our unified pipeline achieves superior performance, improving detection precision (99.5% mAP@0.5), segmentation quality (86% mask IoU), and size estimation (0.10 mm MAE). This represents a 71% reduction in size error compared to segmentation-only models and a 6% boost in detection accuracy over OBB-only methods. This study highlights the potential of advanced deep learning techniques in enhancing the automation and optimisation of quality control in rice milling processes.