YUDO: YOLO for Uniform Directed Object Detection
Đorđe Nedeljković
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.