Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (11): 2662-2668.doi: 10.13229/j.cnki.jdxbgxb20211013

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Muti⁃Object dishes detection algorithm based on improved YOLOv4

Xiang-jiu CHE(),He-yuan CHEN   

  1. College of Computer Science and Technology，Jilin University，Changchun 130012，China
  • Received:2021-10-06 Online:2022-11-01 Published:2022-11-16

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Abstract:

The two-stage object detection algorithm has a slow inference speed， meanwhile， the light-weighted model has a poor performance on small datasets. In view of reasons mentioned above， an improved multi-object detection algorithm based on the algorithm YOLOv4 is proposed. Taking dishes detection as an example， multi-object detection is used to detect and classify the empty dishes. Similar characteristics of the plates' edge are key identity to each completed plate. In order to preserve the salient features， the attention mechanism and pooling methods are added. Furthermore， a light-weighted network can speed up the inference speed and a multi-scale fusion method is able to improve the precision of the model. It turns out that the algorithm proposed improved 4.25% than previous work. According to the comparison with the classic detectors such as Faster RCNN， the FPS is 8-9 times of the latter one. The algorithm in this paper improves the reduced accuracy that the light-weighted models caused， and a more convenient and quick deployment of mobile or embedded devices are able to complete tasks.

Key words: computer application technology, dishes detection, convolutional neural network, multiple object detection

CLC Number: 

  • TP391.4

Fig.1

Architecture of proposed method"

Fig.2

Dataset of empty dishes"

Fig.3

Issue of mosaic"

Fig.4

Max pooling vs soft pooling 1"

Fig.5

Max pooling vs soft pooling 2"

Fig.6

Backbone structure of CA Module"

Table 1

Performance comparison on dishes dataset"

模 型mAP/%Precision/%
Faster RCNN84.1461.87
CenterNet50.0387.68
本文方法76.8080.75
+CA Module80.1482.01
+Soft Module81.0583.83

Table 2

Light-weighted comparison of different models"

模 型FLOPs/BParams/MFPS
Faster RCNN126.5137.117.6
CenterNet34.932.616.7
本文方法6.85.9148.0

Fig.7

Results of detection"

Fig.8

Comparison of feature maps"

Fig.9

Comparison of heatmaps"

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