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Multiscale object detection based on channel and data enhancement at construction sites

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Abstract

Object detection based on computer vision techniques plays an important role in the safety monitoring of large-scene construction sites. However, current object detection algorithms typically have poor performance on small targets. In this study, an enhanced multiscale object detection algorithm is developed to solve the problem of poor detection performance due to scale changes at construction sites. First, a scale-aware data automatic augmentation is defined to learn a data augmentation strategy. Then, to mitigate information loss caused by channel reduction when using feature pyramid network, we propose a method based on subpixel convolution to perform channel enhancement and upsampling, and add a bottom-up path to enhance the complete feature hierarchy with accurate localization signals in the lower layers. Experimental results show that the proposed algorithm achieves better accuracy on the construction site (MOCS) data set and the MS COCO data set. For example, compared with the Faster R-CNN detector with the ResNet-50 backbone network on the MOCS data set and MS COCO data set, the average accuracy increased by \(8.0\%\) and \(1.5\%\), respectively. In particular, the average accuracy of small targets increased by \(10.3\%\) and \(3.4\%\), respectively.

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Acknowledgements

This study was supported in part by the National Natural Science Foundation of China (Nos. 62072024, 41971396, and 61971290), the Research Ability Enhancement Program for Young Teachers of Beijing University of Civil Engineering and Architecture (No. X21024), the Outstanding Youth Program of Beijing University of Civil Engineering and Architecture, the BUCEA Post Graduate Innovation Project, and R &D Program of Beijing Municipal Education Commission(Nos. KM202110016001, KM202210016002).

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Authors and Affiliations

  1. School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Hengyou Wang, Yanfei Song, Linlin Chen & Qiang He

  2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Lianzhi Huo

  3. Institute of Big Data Modeling and Technology, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Hengyou Wang, Linlin Chen & Qiang He

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  1. Hengyou Wang
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  3. Lianzhi Huo
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Correspondence to Hengyou Wang.

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Communicated by B-K Bao.

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Wang, H., Song, Y., Huo, L. et al. Multiscale object detection based on channel and data enhancement at construction sites. Multimedia Systems 29, 49–58 (2023). https://doi.org/10.1007/s00530-022-00983-x

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