Object Detection Algorithm with Dynamic Loss and Enhanced Feature Fusion

Abstract

Object detection is one of the hottest directions in the field of computer vision. In order to further improve the performance of the object detection algorithm, a dynamic intersection over union loss (DYIoU Loss) based on the intersection over union (IoU) is proposed to solve the limitations of the position loss function in the training process. The relationship between the internal components of the position loss function is fully considered, and the weight of the position loss components can be given dynamically at different stages of the training to more specifically constrain the network. This enables the network to optimize different parts more effectively during the early, middle, and late stages of training to better align with the characteristics of the object detection task. In addition, in order to solve the deficiency of the feature fusion stage in the object detection network, deformable convolution is applied to the PAN (path aggregation network) structure, and a deformable path aggregation network neck (DePAN Neck) that can be plugged in is designed to improve the model’s ability to fuse multi-scale features and improve its detection performance on small objects. The above methods are applied to YOLOv6 models of YOLOv6-N, YOLOv6-T and YOLOv6-S sizes, and rich experiments are designed on the COCO2017 dataset to validate the effectiveness. The results show an average increase of 2.0 percentage points in the average precision (mAP).

Keywords

• object detection; deep learning; position loss; feature fusion