Open AccessResearch on Feature Enhancement for Small Object Detection
Author(s) -
Yuyao Tang,
Bingxue Gu,
Bo Jiang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2006/1/012036
Subject(s) - computer science , artificial intelligence , feature (linguistics) , pyramid (geometry) , context (archaeology) , computer vision , residual , noise (video) , object detection , convolution (computer science) , filter (signal processing) , object (grammar) , pattern recognition (psychology) , detector , artificial neural network , image (mathematics) , mathematics , algorithm , paleontology , telecommunications , philosophy , linguistics , geometry , biology
Small object detection is a challenging research direction in the field of computer vision, due to the low resolution and restricted information of small objects. At present, the general detectors only use appearance features to classify and locate objects, but they are prone to failure under the interference of background noise. On the other hand, the detector based on deep neural network has excellent performance on large scale, but it is difficult to extract enough information of small objects. This paper proposes a feature enhancement network (FENet), which contains two modules. The Residual feature enhancement (RFE) module combines residual learning and sub-pixel convolution to improve the resolution of input small objects and remove image noise. The Attention Feature Pyramid (AFP) module integrates the feature pyramid and attention mechanism, which can extract context information and filter redundant context information. At the same time, considering the imbalance of the contribution of large and small objects to the loss function during the training process, a feedback-driven function is introduced to solve the problem of uneven loss under multiple scales. Experimental results show that compared with the existing small object detection methods, our method has better performance.