HODet:Horizontal-to-Oriented Object Detection with simplified annotation

Abstract

Abstract The recognition and positioning of characters on the water gauge are important components of artificial intelligence system for reading ship draft weighing. Meanwhile, the manual annotation of oriented objects has a large workload and low accuracy. To address these issues, this paper proposes a novel oriented detection framework. It introduces three additional parameters to refine the horizontally detected bounding boxes from the model and employs two symmetric functions to confine the angles and scales within specified ranges. In the training and testing of the model, only the horizontal annotation information of the object is needed, reducing the workload of annotation of the target in the dataset. A residual network has been added to the backbone of object detector(YOLO) to enhance the feature extraction capability of deep modules and improve the sensitivity to small objects. For more accurate oriented estimation, we utilize improved Intersection over Union(IoU) to the bounding box regression loss.This method is particularly effective for objects with a dominant orientation and approximately rectangular shapes, such as the characters, vehicles, and buildings commonly found in aerial imagery. A simple implementation of our method has achieved state-of-the-art performances on aerial objects datasets, with a negligible reduction to detection speed. After applying the novel oriented detection method to the intelligent system, real-time testing was conducted on 120 drone videos, resulting in a 35.3% improvement in the accuracy of the system’s water gauge readings.