Detection of residual feed in aquaculture using YOLO and Mask RCNN

Hu, Haigang; Tang, Chao; Dong, Shuanglin; Qian, Yunxia

doi:10.1016/j.aquaeng.2022.102304

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Despite the model being improved compared with previous methods, the results show that some residual foods are not detected. Fine-tuning applied to the pre-trained model can improve the detection results [27]. Jin-Hyun Park et al proposed a method designed with YOLO to detect and count the number of species that are dangerous and can destroy the ecosystem.…”

Section: Related Workmentioning

confidence: 99%

An Improved Deep Learning Model for Underwater Species Recognition in Aquaculture

Hamzaoui,

Ould-Elhassen Aoueileyine,

Romdhani

et al. 2023

Fishes

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The ability to differentiate between various fish species plays an essential role in aquaculture. It helps to protect their populations and monitor their health situations and their nutrient systems. However, old machine learning methods are unable to detect objects in images with complex backgrounds and especially in low-light conditions. This paper aims to improve the performance of a YOLO v5 model for fish recognition and classification. In the context of transfer learning, our improved model FishDETECT uses the pre-trained FishMask model. Then it is tested in various complex scenes. The experimental results show that FishDETECT is more effective than a simple YOLO v5 model. Using the evaluation metrics Precision, Recall, and mAP50, our new model achieved accuracy rates of 0.962, 0.978, and 0.995, respectively.

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Section: Related Workmentioning

confidence: 99%

An Improved Deep Learning Model for Underwater Species Recognition in Aquaculture

Hamzaoui,

Ould-Elhassen Aoueileyine,

Romdhani

et al. 2023

Fishes

View full text Add to dashboard Cite

show abstract

“…Underwater target detection technology is vital not only for military maritime defense tasks [ [1] , [2] , [3] , [4] ] but also for ecological environmental protection [ 5 , 6 ], and critical economic sectors, including fisheries and aquaculture [ [7] , [8] , [9] , [10] ]. This paper focuses on enhancing target detection performance in complex underwater environments.…”

Section: Introductionmentioning

confidence: 99%

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Lian-suo,

Shen-hao,

Long-yu

2024

Heliyon

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“…This model exhibited an average detection time of 3.289 f/s when operating at a resolution of 3000 × 3000. Reference 21 proposed a dual-strategy detection algorithm for feed residues in farming areas. The algorithm is improved based on YOLOv3, while using the mask RCNN.…”

Section: Introductionmentioning

confidence: 99%

YOLO-Granada: A Lightweight Attentioned Yolo forPomegranates Fruit Detection

Zhao,

Li,

Guo

et al. 2024

Preprint

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Pomegranate is an important fruit crop that is usually managed manually through experience. Intelligent management systems for pomegranate orchards can improve yields and address labor shortages. Fast and accurate detection of pomegranates is one of the key technologies of this management system, crucial for yield and scientific management. Currently, most solutions use deep learning to achieve pomegranate detection, but deep learning is not effective in detecting small targets and large parameters, and the computation speed is slow; therefore, there is room for improving the pomegranate detection task. Based on the improved You Only Look Once version 5 (YOLOv5) algorithm, a lightweight pomegranate growth period detection algorithm YOLO-Granada is proposed. A lightweight ShuffleNetv2 network is used as the backbone to extract pomegranate features. Using grouped convolution reduces the computational effort of ordinary convolution, and using channel shuffle increases the interaction between different channels. In addition, the attention mechanism can help the neural network suppress less significant features in the channels or space, and the Convolutional Block Attention Module (CBAM) attention mechanism can improve the effect of attention and optimize the object detection accuracy by using the contribution factor of weights. The average accuracy of the improved network reaches 0.922. It is only less than 1% lower than the original YOLOv5s model (0.929) but brings a speed increase and a compression of the model size. and the detection speed is 17.3% faster than the original network. The parameters, floating-point operations, and model size of this network are compressed to 54.7%, 51.3%, and 56.3% of the original network, respectively. In addition, the algorithm detects 8.66 images per second, achieving real-time results. In this study, the Nihui convolutional neural network (NCNN) framework was further utilized to develop an Android-based application(app) for real-time pomegranate detection. The method provides a more accurate and lightweight solution for intelligent management devices in pomegranate orchards, which can provide a reference for the design of neural networks in agricultural applications.

show abstract

Detection of residual feed in aquaculture using YOLO and Mask RCNN

Cited by 12 publications

References 10 publications

An Improved Deep Learning Model for Underwater Species Recognition in Aquaculture

An Improved Deep Learning Model for Underwater Species Recognition in Aquaculture

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

YOLO-Granada: A Lightweight Attentioned Yolo forPomegranates Fruit Detection

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