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Attention Aware Deep Learning Object Detection and Simulation

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Simulation Tools and Techniques (SIMUtools 2020)

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Abstract

Dish recognition has certain difficulties in specific applications. Because in the actual inspection, the dishes are filled with food, and the food occupy most of the space of the dishes, and only the edges of the dishes can be seen. If you use empty dishes for training, the accuracy will be low due to insufficient feature matching during actual detection. At the same time, due to the wide variety of foods, if we collect all the food during training, the pre-processing workload will be very large. Based on the above ideas, this paper analyzes the model through three visualization methods, improves Faster R-CNN, and proposes a Cross Faster R-CNN model. This model consists of Faster R-CNN and Cross Layer, which can fuse the low-level features and high-level features of dishes. During training, the model can focus the feature extraction on the edges of the dishes, reducing the interference of food on dish recognition. This method improves the detection accuracy without significantly increasing the detection time. The experimental results show that compared with Faster R-CNN, the accuracy and recall of Cross Faster R-CNN have increased to a certain extent, and the detection speed has basically not changed significantly.

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Authors and Affiliations

  1. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321300, China

    Jiping Xiong, Lingyun Zhu, Lingfeng Ye & Jinhong Li

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  1. Jiping Xiong
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  2. Lingyun Zhu
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  3. Lingfeng Ye
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  4. Jinhong Li
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Correspondence to Jiping Xiong .

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Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Xiong, J., Zhu, L., Ye, L., Li, J. (2021). Attention Aware Deep Learning Object Detection and Simulation. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-72795-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72794-9

  • Online ISBN: 978-3-030-72795-6

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