Small-Object Detection Based on YOLO and Dense Block via Image Super-Resolution

• Published in: IEEE access Vol. 9; pp. 56416 - 56429
• Main Authors: Wang, Zhuang-Zhuang, Xie, Kai, Zhang, Xin-Yu, Chen, Hua-Quan, Wen, Chang, He, Jian-Biao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: balance loss function; Computer networks; Computer vision; dense block; Detectors; Feature extraction; foreground and background; Head; Image resolution; image super-resolution; multi-scale feature fusion; Object detection; Object recognition; Small-object detection; Superresolution; Target detection; Target recognition; Task analysis; Traffic signs; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3072211

Small-object detection is a basic and challenging problem in computer vision tasks. It is widely used in pedestrian detection, traffic sign detection, and other fields. This paper proposes a deep learning small-object detection method based on image super-resolution to improve the speed and accuracy of small-object detection. First, we add a feature texture transfer (FTT) module at the input end to improve the image resolution at this end as well as to remove the noise in the image. Then, in the backbone network, using the Darknet53 framework, we use dense blocks to replace residual blocks to reduce the number of network structure parameters to avoid unnecessary calculations. Then, to make full use of the features of small targets in the image, the neck uses a combination of SPPnet and PANnet to complete this part of the multi-scale feature fusion work. Finally, the problem of image background and foreground imbalance is solved by adding the foreground and background balance loss function to the YOLOv4 loss function part. The results of the experiment conducted using our self-built dataset show that the proposed method has higher accuracy and speed compared with the currently available small-target detection methods.