YUDO: YOLO for Uniform Directed Object Detection
This work addresses directed object detection for applications like honeybee tracking, but it is incremental as it adapts existing YOLO methods to a specific domain.
The paper tackles the problem of detecting uniformly-sized directed objects by predicting center coordinates and direction angles, achieving this with a customized YOLOv7 architecture that uses only one detection head without anchors and introduces DirIoU for rotated box evaluation.
This paper presents an efficient way of detecting directed objects by predicting their center coordinates and direction angle. Since the objects are of uniform size, the proposed model works without predicting the object's width and height. The dataset used for this problem is presented in Honeybee Segmentation and Tracking Datasets project. One of the contributions of this work is an examination of the ability of the standard real-time object detection architecture like YoloV7 to be customized for position and direction detection. A very efficient, tiny version of the architecture is used in this approach. Moreover, only one of three detection heads without anchors is sufficient for this task. We also introduce the extended Skew Intersection over Union (SkewIoU) calculation for rotated boxes - directed IoU (DirIoU), which includes an absolute angle difference. DirIoU is used both in the matching procedure of target and predicted bounding boxes for mAP calculation, and in the NMS filtering procedure. The code and models are available at https://github.com/djordjened92/yudo.