CME-YOLOv5: An Efficient Object Detection Network for Densely Spaced Fish and Small Targets

Li, Jianyuan; Liu, Chunna; Lu, Xiaochun; Wu, Bilang

doi:10.3390/w14152412

Cited by 41 publications

(19 citation statements)

References 17 publications

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“…However, it is important to remember that the predetermined categories of plant diseases and pests do not always align with actual results. While the detection network may provide accurate results in different patterns, these patterns may not YOLOv5 architecture (Li et al, 2022).…”

Section: One-stage Network Based Plant Lesion Detectionmentioning

confidence: 99%

An advanced deep learning models-based plant disease detection: A review of recent research

et al. 2023

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Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

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Section: One-stage Network Based Plant Lesion Detectionmentioning

confidence: 99%

An advanced deep learning models-based plant disease detection: A review of recent research

et al. 2023

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“…To address the problem of detecting small fish [33] presented a YOLOv5-based model. It tries to address the issues of inaccurate location and insufficient information for detecting underwater targets.…”

Section: Related Workmentioning

confidence: 99%

Investigation of You Only Look Once Networks for Vision-based Small Object Detection

Yang¹

2023

IJACSA

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Small object detection is a challenging issue in computer vision-based algorithms. Although various methods have been investigated for common objects including person, car and others, small object are not addressed in this issue. Therefore, it is necessary to conduct more researches on them. This paper is focused on small object detection especially jewellery as current object detection methods suffer from low accuracy in this domain. This paper introduces a new dataset whose images were taken by a web camera from a jewellery store and data augmentation procedure. It comprises three classes, namely, ring, earrings, and pendant. In view of the small target of jewellery and the real-time detection, this study adopted the You Only Look Once (Yolo) algorithms. Different Yolo based model including eight versions are implemented and train them using our dataset to address most effective one. Evaluation criteria, including accuracy, F1 score, recall, and mAP, are used to evaluate the performance of the various YOLOv5, YOLOv6, and YOLOv7 versions. According to the experimental findings, utilizing YOLOv6 is significantly superior to YOLOv7 and marginally superior to YOLOv5.

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“…CME-YOLOv5 for Species Identification: Li et al [ 20 ] introduced this model in 2022, which stood out for identifying distinctive fish species with a high mAP of 92.3%.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8-C2f-Faster-EMA: An Improved Underwater Trash Detection Model Based on YOLOv8

Zhu,

Hu,

Zheng

et al. 2024

Sensors

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Anthropogenic waste deposition in aquatic environments precipitates a decline in water quality, engendering pollution that adversely impacts human health, ecological integrity, and economic endeavors. The evolution of underwater robotic technologies heralds a new era in the timely identification and extraction of submerged litter, offering a proactive measure against the scourge of water pollution. This study introduces a refined YOLOv8-based algorithm tailored for the enhanced detection of small-scale underwater debris, aiming to mitigate the prevalent challenges of high miss and false detection rates in aquatic settings. The research presents the YOLOv8-C2f-Faster-EMA algorithm, which optimizes the backbone, neck layer, and C2f module for underwater characteristics and incorporates an effective attention mechanism. This algorithm improves the accuracy of underwater litter detection while simplifying the computational model. Empirical evidence underscores the superiority of this method over the conventional YOLOv8n framework, manifesting in a significant uplift in detection performance. Notably, the proposed method realized a 6.7% increase in precision (P), a 4.1% surge in recall (R), and a 5% enhancement in mean average precision (mAP). Transcending its foundational utility in marine conservation, this methodology harbors potential for subsequent integration into remote sensing ventures. Such an adaptation could substantially enhance the precision of detection models, particularly in the realm of localized surveillance, thereby broadening the scope of its applicability and impact.

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CME-YOLOv5: An Efficient Object Detection Network for Densely Spaced Fish and Small Targets

Cited by 41 publications

References 17 publications

An advanced deep learning models-based plant disease detection: A review of recent research

An advanced deep learning models-based plant disease detection: A review of recent research

Investigation of You Only Look Once Networks for Vision-based Small Object Detection

YOLOv8-C2f-Faster-EMA: An Improved Underwater Trash Detection Model Based on YOLOv8

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