A Rice Grain Detection Method Based on Rotation-decoupled Detector for Oriented Object

Abstract

【Objective】In rice seed investigations, the detection methods for rice grain counting and aspect ratios estimating were established based on computer vision technology, which could save labor costs and improve efficiency. It is conductive to the establishment of digital breeding system.【Method】The feasibility of rice detection methods was verified through comparing the horizontal bounding boxes (HBB) detection strategy based on model of YOLOv5l and oriented bounding boxes (OBB) detection strategy based on models of ResNet101 and DarkNet53. Meanwhile, the differences between the rice grain aspect ratios predicted by the OBB detection strategy and the actual measured values were compared.【Result】No significant difference was found between the aspect ratios of rice grains calculated by the OBB detection models and the measured values, and both the values of root mean square error (RMSE) ranged from 0.92 to 2.29. The aspect ratios calculated by HBB detection models were significantly lower than the measured values, with greater values of RMSE ranging from 9.38 to 9.45. The accuracies of OBB detection strategy are almost equivalent to those of HBB detection strategy in rice grain counting. The OBB detectors could accurately calculate the aspect ratios of rice grains, while the HBB detectors could not.【Conclusion】The study results demonstrated that the OBB detection could count rice grains accurately. Compared with the traditional target detection strategy of HBB, it could reduce the background noise, lower the inaccuracy of densely distributed objects, and calculate the aspect ratios of rice grains accurately. The method used in the study could be further applied to calculation of length, width and size of rice grains, detection of seeds quality, identification of seed cultivars and other digital breeding system.

Keywords

• rice
• grain detection
• oriented bounding box
• aspect ratio
• object detection
• computer vision