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A detection method for low-pixel ratio object
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DOI：10.1007/s11042-018-6653-6.png)
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Abstract
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Low-pixel object detection is a kind of difficult program. Existing object detection benchmarks and methods mainly focus on standard detection task. However, these way cannot get good performance on low-pixel ratio object detection, which has a few pixel in high resolution images. In order to solve it, we propose a new deep learning framework. This framework improves Faster R-CNN by combining multiple level feature map and optimizing anchor size for bounding box recognition. In order to validate our approach, we collect and annotate a dataset for road garbage detection, which contains 801 images and 966 bounding boxes. Experiments demonstrate that our framework outperforms other state-of-the-art detection methods. What's more, our method can apply on road garbage target.
Keywords:
Convolutional neural network
Object detection
Road garbage detection
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