基于位置可学习视觉中心机制的零售商品检测方法

doi:10.11959/j.issn.2096-3750.2023.00366

Abstract

Abstract:

To address the problem of low detection accuracy caused by the difficulty in effectively capturing significant and diversified feature information for packaging deformation and overlap products, a location learnable visual center (LLVC) mechanism was designed to improve YOLOX-s, achieving higher detection accuracy.To effectively deal with product packaging deformation and overlap phenomena, firstly, global context information was captured through a lightweight multi-layer perceptron to help the model better understand spatial information in product features.Secondly, the local feature representation ability was enhanced by the designed LLVC and the spatial information was used to allocate learnable weights for local features to increase the attention of discriminative local features.Finally, the intersection over union (IoU) loss function was replaced with centered intersection over union (CIoU) and power parameters were introduced on this basis to effectively reduce the missed detection rate.Experimental results show that the proposed method achieves an accuracy of 91.3% on the retail product checkout (RPC) dataset, which is 2.2% higher than YOLOX-s and better than current mainstream lightweight object detection algorithms.At the same time, frame per second (FPS) is 97 frame/s, and the model size is 9.48 MB.It can accurately and in real-time detect retail products in scenarios where computing resources are limited.

Key words: retail commodity detection, YOLOX-s, central learning mechanism, loss function, lightweight

CLC Number:

TP18

Xiaohua LYU, Mingchen WEI, Libo LIU. Retail commodity detection method based on location learnable visual center mechanism[J]. Chinese Journal on Internet of Things, 2023, 7(4): 142-152.

Figures/Tables 12

References 30

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方法	mAP	params/MB	FPS/(frame·s^-1)
YOLOX-s (baseline)	89.1%	9.0	102
YOLOX-s (MLP)	89.8%	9.21	100
YOLOX-s (LLVC）	90.1%	9.29	100
YOLOX-s (CIoU-α)	89.5%	9.0	102
YOLOX-s	91.3%	9.48	97
(MLP＆LLVC＆CIoU-α)

方法		mAP
方法	简单模式	中等模式	复杂模式
YOLOX-s	87.1%	85.9%	82.6%
YOLOX-MLC	89.5%	88.9%	86.8%

方法	mAP	params/MB	FPS/(frame·s^-1)
YOLOv3	87.3%	63.0	74
YOLOv3-Tiny	72.2%	8.0	277
YOLOv4-Tiny	74.4%	6.1	270
YOLOv5-s	86.8%	7.3	156
YOLOv7-Tiny	85.2%	6.1	286
YOLOX-Tiny	81.9%	5.06	296
YOLOX-s	89.1%	9.0	102
YOLOv8-s	90.5%	12.5	151
YOLOX-MLC	91.3%	9.48	97

Retail commodity detection method based on location learnable visual center mechanism

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