GARBAGE DETECTION BASED ON LGD-YOLO HIGH PRECISION LIGHTWEIGHT OBJECT DETECTION NETWORK

XIAO Lizhong; HU Fan

doi:10.13205/j.hjgc.202406020

Volume 42 Issue 6

Jun. 2024

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2024 > 42(6): 169-177

XIAO Lizhong, HU Fan. GARBAGE DETECTION BASED ON LGD-YOLO HIGH PRECISION LIGHTWEIGHT OBJECT DETECTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 169-177. doi: 10.13205/j.hjgc.202406020

Citation:

XIAO Lizhong, HU Fan. GARBAGE DETECTION BASED ON LGD-YOLO HIGH PRECISION LIGHTWEIGHT OBJECT DETECTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 169-177. doi: 10.13205/j.hjgc.202406020

XIAO Lizhong, HU Fan. GARBAGE DETECTION BASED ON LGD-YOLO HIGH PRECISION LIGHTWEIGHT OBJECT DETECTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 169-177. doi: 10.13205/j.hjgc.202406020

Citation:

XIAO Lizhong, HU Fan. GARBAGE DETECTION BASED ON LGD-YOLO HIGH PRECISION LIGHTWEIGHT OBJECT DETECTION NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 169-177. doi: 10.13205/j.hjgc.202406020

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GARBAGE DETECTION BASED ON LGD-YOLO HIGH PRECISION LIGHTWEIGHT OBJECT DETECTION NETWORK

doi: 10.13205/j.hjgc.202406020

XIAO Lizhong,
HU Fan^,

School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China

Received Date: 2023-05-23
Available Online: 2024-07-11

Abstract

Abstract

At present, with the rapid development of society and the rapid growth of the urban population, garbage pollution has become increasingly prominent, and garbage classification is imperative. Manual processing has the problems of heavy tasks and low efficiency, and some automated classification methods have low detection accuracy and slow speed. To improve the accuracy of garbage detection in complex scenes, lighten the structure, and make it easier to deploy, an improved YOLO v5s garbage detection model, Lightweight Garbage Detection YOLO (LGD-YOLO) was proposed, which integrated a lightweight convolution module, attention mechanism, and multiple receptive field modules. First, Ghost convolution and Slim Neck module including GSConv were introduced into the network structure to make the model lighter. Secondly, the coordinate attention mechanism was embedded to focus on important information to improve the detection accuracy. Finally, the multi-receptive field module was introduced to improve the multi-scale detection capability of the model and avoid missing detection of small target objects. The data set containing trash garbage images in different environments was tested and verified. The experimental results showed that the parameters and calculation amount of the improved model were 5.77 M and 9.2 GFLOPs, respectively, which were 22.4% and 56.4% less than the original model. The single image detection speed was 26.5 ms, meeting the real-time requirements of garbage detection. In addition, the improved algorithm has good detection accuracy, with mAP_0.5 and mAP_0.5∶0.95 reaching 96.20% and 77.77%, respectively, which was superior to the current popular target detection algorithm.
- garbage classification,
- object detection,
- lightweight network,
- coordinate attention,
- multiscale detection

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References(21)

References

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