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An Efficient Detector for Automatic Tomato Classification Systems
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DOI：10.1109/ACCESS.2024.3522349.png)
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Abstract
En 中文
Nowadays, artificial intelligence and robotics have been deployed in almost all areas of human life. Especially in agriculture, it has helped people free up labor, speed up production, and ensure product quality. This research aims to develop a vision-based tomato detector to support robots and automatic tomato classification systems. In this idea, the original convolution blocks in the Backbone and Neck modules of the YOLOv8n architecture are replaced by a new version, called Receptive Field Attention Convolution (RFAConv). The model was trained and evaluated using four benchmarks, including the Laboro Tomato, Tomato Plantfactory, Sai Gon University, and CubeAI datasets. It achieved the best performance at 89.8% of mAP@0.5 and 65.2% of mAP@0.5:0.95 on the Tomato Plantfactory dataset. These results show that the proposed network is superior to others under a fixed set of experimental conditions. The video demonstration, datasets, weights, and models are available at https://bit.ly/3XdLrLM.
Keywords:
Convolution
Feature extraction
Kernel
Computer architecture
YOLO
Radio frequency
Neck
Robots
Head
Detectors
Convolutional neural network (CNN)
tomato detection and classification
receptive field attention convolution (RFAConv)
YOLOv8n
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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