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Small Object Detection using Context and Attention
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- Authors
- Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee
- Issue Date
- 2021-04
- Citation
- International Conference on Artificial Intelligence in Information and Communication (ICAIIC) 2021, pp.181-186
- Language
- English
- Type
- Conference Paper
- Project Code
- 21HS1600, Development of AI Technology for Guidance of a Mobile Robot to its Goal with Uncertain Maps in Indoor/Outdoor Environments, Lee Jae-Yeong
- Abstract
- There are many limitations applying object detection algorithm on various environments. Specifically, detecting small objects is still challenging because they have low-resolution and limited information. We propose an object detection method using context for improving accuracy of detecting small objects. The proposed method uses additional features from different layers as context by concatenating multi-scale features. We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. Moreover, for $300 \times 300$ input, we achieved 78.1% Mean Average Precision (mAP) on the PASCAL VOC2007 test set.
- KSP Keywords
- Attention mechanism, Contextual information, Detection Method, Detection algorithm, Different layers, Improving accuracy, Limited information, Multi-scale, Test Set, low resolution, mean average precision