Version 1
: Received: 27 April 2024 / Approved: 29 April 2024 / Online: 29 April 2024 (11:14:53 CEST)
How to cite:
Chen, H.; Zhang, R.; Peng, J.; Peng, H.; Hu, W.; Wang, Y.; Jiang, P. Yolo-Chili: An Efficient Lightweight Network Model for Localization of Pepper Picking in Complex Environments. Preprints2024, 2024041916. https://doi.org/10.20944/preprints202404.1916.v1
Chen, H.; Zhang, R.; Peng, J.; Peng, H.; Hu, W.; Wang, Y.; Jiang, P. Yolo-Chili: An Efficient Lightweight Network Model for Localization of Pepper Picking in Complex Environments. Preprints 2024, 2024041916. https://doi.org/10.20944/preprints202404.1916.v1
Chen, H.; Zhang, R.; Peng, J.; Peng, H.; Hu, W.; Wang, Y.; Jiang, P. Yolo-Chili: An Efficient Lightweight Network Model for Localization of Pepper Picking in Complex Environments. Preprints2024, 2024041916. https://doi.org/10.20944/preprints202404.1916.v1
APA Style
Chen, H., Zhang, R., Peng, J., Peng, H., Hu, W., Wang, Y., & Jiang, P. (2024). Yolo-Chili: An Efficient Lightweight Network Model for Localization of Pepper Picking in Complex Environments. Preprints. https://doi.org/10.20944/preprints202404.1916.v1
Chicago/Turabian Style
Chen, H., Yi Wang and Ping Jiang. 2024 "Yolo-Chili: An Efficient Lightweight Network Model for Localization of Pepper Picking in Complex Environments" Preprints. https://doi.org/10.20944/preprints202404.1916.v1
Abstract
Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.
Keywords
Chili Detection,Automatic Picking,Neural Network,Model Compression
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
