Abstract
Since the introduction of convolutional neural networks, object detection based on deep learning has made great progress and has been widely used in the industry. However, because the weak and small object contains too little information, the samples are rich in diversity, and there are different degrees of occlusion, the detection difficulty is too great, and the object detection has entered a bottleneck period of development. We firstly introduce a super-resolution network to solve the problem of small object pixel area being too small, and fuse the super-resolution generator with the object detection baseline model for collaborative training. In addition, in order to reinforce the weak feature of small objects, we design a convolution block based on the edge detection operator Sobel. Experiments show that proposed method achieves mAP50 improvement of 2.4% for all classes and 4.4% for the relatively weak pedestrian class on our dataset relative to the Yolov5s baseline model.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62171347,61877066, 61771379,62001355,62101405; the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621005; the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY03-05 and 2021ZDLGY02-08; the Science and Technology Program in Xi’an of China under Grant XA2020-RGZNTJ-0021; 111 Project.
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Hou, B., Chen, X., Zhou, S., Jiang, H., Wang, H. (2022). SR-YOLO: Small Objects Detection Based on Super Resolution. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_38
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DOI: https://doi.org/10.1007/978-3-031-14903-0_38
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