Balanced One-Stage Object Detection by Enhancing the Effect of Positive Samples
Zuyi Wang, Wenjun Zhu, Wei Zhao, Li Xu
Abstract
The one-stage object detector has recently attracted extensive interest due to its high detection efficiency and simple framework. However, one-stage detectors suffer much from the extreme positive-negative imbalance since the majority of samples are labeled as the negative. To address this problem, a novel activation function, the Gradient Enhanced Function (GEF), is devised for the last output layer of the one-stage detector to strengthen the role of positive samples during the optimization process. The Quality-Guided Loss (QGL) is proposed for the classification task in object detection, which further makes the training focus on the high-quality positive samples. In addition, QGL is devised to handle different classification labels (i.e. the one-hot label and the soft label). Owing to its simplicity and effectiveness, the QGL together with the GEF might be applicable to various one-stage methods, including anchor-based and anchor-free detectors. Comprehensive experiments are conducted on multiple public benchmarks, MS COCO, PASCAL VOC and Berkeley DeepDrive. The results demonstrate that the proposed approach achieves notable improvements in both anchor-based and anchor-free detectors with various classification labels. The effectiveness of the GEF and the QGL is further verified in the stronger one-stage detectors.