Small target detection with remote sensing images based on an improved YOLOv5 algorithm

Pei, Wenjing; Shi, Zhanhao; Gong, Kai

doi:10.3389/fnbot.2022.1074862

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…As a significant field of target detection technology, You Only Look Once (YOLO)v5 locates and identifies objects by learning their features in images or videos. It excels at detecting small or intricate objects with several benefits such as real-time and high-speed performance, accurate multi-scale detection, lightweight design, and intuitive usability and good compatibility with deep learning [2]. In the early YOLO mask detection models [3], the focus was mainly on local features, lacking the fusion of global information.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing mask detection performance based on YOLOv5 model optimization and attention mechanisms

Yang

2024

ACE

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Due to the COVID-19 pandemic, there has been a significant increase in the usage of masks, leading to more complex scenarios for mask detection techniques. This paper focuses on optimizing the performance of mask detection using the You Only Look Once (YOLO) v5 model. In this study, the yolov5 target detection model was employed for training the mask dataset. Diverse model improvement techniques were explored to enhance the model's capability to capture crucial features and differentiate masks from the background in complex scenarios. Finally, the modified model was compared with the earlier original target detection model to identify the most considerable performance gain. The CSPDarknet design with the TensorFlow framework is utilized in this study, and the Attention Mechanism module is implemented through the Keras library. The objective is to optimize the three feature layers between the backbone network and the neck by integrating multiple attention mechanisms. This will enable the model to more quickly and accurately capture important features when dealing with complex scenarios by adjusting the feature map weights. Additionally, in the feature pyramid network, shallow feature maps are fused with deeper feature maps in a certain order to determine the most efficient feature fusion method. Finally, this study identified the optimal combination of attention mechanism and feature fusion through ablation experiments. The results of the experiment demonstrate that the combination of SE block and shallow feature fusion (SE + FF2 model) can greatly enhance category confidence, leading to an improved model performance.

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Section: Introductionmentioning

confidence: 99%

“…As such, the model's accuracy needs further enhancement. Therefore, this research aims to optimize the YOLOv5 model to improve mask detection performance and obtain higher accuracy [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing mask detection performance based on YOLOv5 model optimization and attention mechanisms

Yang

2024

ACE

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“…Yang and Tong (2022) proposed a visual multi-scale attention module based on the YOLOv3 algorithm, which integrated feature maps of different scales with attention weights to eliminate the interference information of traffic sign features. Pei et al (2023) proposed an LCB-YOLOv5 algorithm to detect small targets in remote sensing images. This method improves the accuracy of small target detection by introducing more receptive field and replacing the EIOU loss function.…”

Section: Introductionmentioning

confidence: 99%

E-YOLOv4-tiny: a traffic sign detection algorithm for urban road scenarios

et al. 2023

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IntroductionIn urban road scenes, due to the small size of traffic signs and the large amount of surrounding interference information, current methods are difficult to achieve good detection results in the field of unmanned driving.MethodsTo address the aforementioned challenges, this paper proposes an improved E-YOLOv4-tiny based on the YOLOv4-tiny. Firstly, this article constructs an efficient layer aggregation lightweight block with deep separable convolutions to enhance the feature extraction ability of the backbone. Secondly, this paper presents a feature fusion refinement module aimed at fully integrating multi-scale features. Moreover, this module incorporates our proposed efficient coordinate attention for refining interference information during feature transfer. Finally, this article proposes an improved S-RFB to add contextual feature information to the network, further enhancing the accuracy of traffic sign detection.Results and discussionThe method in this paper is tested on the CCTSDB dataset and the Tsinghua-Tencent 100K dataset. The experimental results show that the proposed method outperforms the original YOLOv4-tiny in traffic sign detection with 3.76% and 7.37% improvement in mAP, respectively, and 21% reduction in the number of parameters. Compared with other advanced methods, the method proposed in this paper achieves a better balance between accuracy, real-time performance, and the number of model parameters, which has better application value.

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A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Chen,

Wu,

et al. 2023

IEEE Access

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A printed circuit board (PCB) functions as a substrate essential for interconnecting and securing electronic components. Its widespread integration is evident in modern electronic devices, spanning computers, cell phones, televisions, digital cameras, and diverse apparatus. Ensuring product quality mandates meticulous defect inspection, a task exacerbated by the heightened precision of contemporary circuit boards, intensifying the challenge of defect detection. Conventional algorithms, hampered by inefficiency and limited accuracy, fall short of usage benchmarks. In contrast, PCB defect detection algorithms rooted in deep learning hold promise for achieving heightened accuracy and efficiency, bolstered by their adeptness at discerning novel defect types. This review presents a comprehensive analysis of machine vision-based PCB defect detection algorithms, traversing the realms of machine learning and deep learning. It commences by contextualizing and elucidating the significance of such algorithms, followed by an extensive exploration of their evolution within the machine vision framework, encompassing classification, comparison, and analysis of algorithmic principles, strengths, and weaknesses. Moreover, the introduction of widely used PCB defect detection datasets and assessment indices enhances the evaluation of algorithmic performance. Currently, the detection accuracy can exceed 95% at an Intersection over Union (IoU) of 0.5. Lastly, potential future research directions are identified to address the existing issues in the current algorithm. These directions include utilizing Transformers as a foundational framework for creating new algorithms and employing techniques like Generative Adversarial Networks (GANs) and reinforcement learning to enhance PCB defect detection performance.

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Small target detection with remote sensing images based on an improved YOLOv5 algorithm

Cited by 6 publications

References 48 publications

Enhancing mask detection performance based on YOLOv5 model optimization and attention mechanisms

Enhancing mask detection performance based on YOLOv5 model optimization and attention mechanisms

E-YOLOv4-tiny: a traffic sign detection algorithm for urban road scenarios

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

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