Robust one-stage object detection with location-aware classifiers
| Autor: | Anda Cheng, Yifan Zhang, Wanguo Wang, Qiang Chen, Jian Cheng, Peisong Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning Detector One stage Pattern recognition 02 engineering and technology 01 natural sciences Object detection Artificial Intelligence Robustness (computer science) 0103 physical sciences Signal Processing Location aware 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence 010306 general physics business Classifier (UML) Software |
| Zdroj: | Pattern Recognition. 105:107334 |
| ISSN: | 0031-3203 |
| DOI: | 10.1016/j.patcog.2020.107334 |
| Popis: | Recent progress on one-stage detectors focuses on improving the quality of bounding boxes, while they pay less attention to the classification head. In this work, we focus on investigating the influence of the classification head. To understand the behavior of the classifier in one-stage detectors, we resort to the methods of the Explainable deep learning area. We visualize its learned representations via activation maps and analyze its robustness to image scene context. Based on the analysis, we observe that the classifier limits the performance of the detector due to its limited receptive field and the lack of object locations. Then, we design a simple but efficient location-aware multi-dilation module (LAMD) to enhance the weak classifier. We conduct extensive experiments on the COCO benchmark to validate the effectiveness of LAMD. The results suggest that our LAMD can achieve consistent improvements and leads to robust detection across various one-stage detectors with different backbones. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect