Real-Oriented Object Detection Driven by Intelligent Stockbreeding

Detecting objects with inherent orientations has numerous applications in the context of livestock reproduction. In this new scenario, the inherent orientations in the range [0, 2π) of target objects are detected alongside their bounding boxes to produce real-oriented bounding boxes. Due to the 0-to-2π orientation angle, however, traditional oriented IOU algorithms and mean-squared error on orientation are incapable of determining the similarity between two real-oriented bounding boxes. Therefore, we propose an orientation-sensitive pseudo-IOU algorithm and periodic loss of orientation to adapt to the new scenario of oriented object detection. The detection workflow is implemented by extending the YOLO head. Experimental results indicate that the detector equipped with the minor-angle-based pseudo-IOU (MinorAngle) and cosine form of orientation-related loss (L
DirectCOS
) presents the best performance and outperforms the state-of-the-art techniques. We hope that these discoveries will stimulate the community.