Research of object detection method based on DCGAN data-set enhancement technique

Dunhuang Shi; Yanan Yu; Huiping Li

doi:10.1117/12.2606531

24 November 2021 Research of object detection method based on DCGAN data-set enhancement technique

Dunhuang Shi, Yanan Yu, Huiping Li

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 120690Q (2021) https://doi.org/10.1117/12.2606531
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

With the rise of the new generation of artificial intelligence technology, the object detection method based on deep learning has achieved remarkable results. In this paper, the detection accuracy of three popular object detection algorithms such as You Only Look Once (YOLO V3), Region-CNN (Faster R-CNN) and Single Shot MultiBox Detector (SSD) has been compared. Aiming at the actual detection problems of building block parts with irregular shape and different sizes, a method that combines deep convolutional generative adversarial networks (DCGAN) with deep learning based object detection algorithm is proposed to solve the problems of over fitting or weak generalization ability in the case of limited datasets, and to improve the detection accuracy of object detection algorithm. Experimental results show that: 1. Using public datasets, when the training data is reduced, the mean average precision (mAP) values of the above three algorithms are reduced respectively. Among those, SSD algorithm has the smallest change, which decreases 7.81%. 2. The control variable method is used to train the building block parts. In the case of insufficient training data, the detection accuracy of three object detection algorithms is low. 3. After combining SSD algorithm with DCGAN algorithm and applying it into the detection task of building block parts, the mAP value is improved from 79.63% to 83.32%, and the detection accuracy is obviously improved.

Citation Download Citation

Dunhuang Shi, Yanan Yu, and Huiping Li "Research of object detection method based on DCGAN data-set enhancement technique", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 120690Q (24 November 2021); https://doi.org/10.1117/12.2606531

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KEYWORDS

Detection and tracking algorithms

Target detection

Evolutionary algorithms

Image segmentation

Neural networks

Data modeling

Convolution

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