YOLO-Extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images

Liu, Zhiguo; Gao, Yuan; Du, Qianqian; Chen, Meng; Lv, Wenqiang

doi:10.1109/access.2023.3233964

Cited by 60 publications

(34 citation statements)

References 33 publications

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“…In-field wheat field backgrounds are complex, with poor illumination conditions, mutual overlapping occlusion between heads and between heads and leaves, and blurring of wheat heads caused by shooting, all of which affect the accuracy of wheat head recognition. Dong [35], Liu [36], and other researchers have all proposed that the overlapping occlusion of wheat heads will cause difficulties in detection. In this study, these three types of images were selected as the test set to compare the correlation between the predicted wheat heads of YOLOv7-MA, YOLOv7, YOLOX, YOLOv5, and Faster-RCNN and manual counting results.…”

Section: Discussionmentioning

confidence: 99%

“…Dong et al [35] introduced a random polarized self-attention mechanism, SPSA, in both spatial and channel dimensions and effectively combined it with a random unit to improve the detection capability of overlapping and occluded wheat heads. Liu et al [36] proposed the YOLO-Extract algorithm based on YOLOv5, which enhances the feature extraction capability of wheat heads by introducing a coordinated attention mechanism. Zhou et al [37] presented the multi-window swine transformer network, which combines self-attention mechanisms and feature pyramid networks to extract multi-scale features and effectively improve the accuracy of detecting complex wheat heads in the field.…”

Section: Introductionmentioning

confidence: 99%

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YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Meng,

Li,

et al. 2023

Remote Sensing

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Detection and counting of wheat heads are crucial for wheat yield estimation. To address the issues of overlapping and small volumes of wheat heads on complex backgrounds, this paper proposes the YOLOv7-MA model. By introducing micro-scale detection layers and the convolutional block attention module, the model enhances the target information of wheat heads and weakens the background information, thereby strengthening its ability to detect small wheat heads and improving the detection performance. Experimental results indicate that after being trained and tested on the Global Wheat Head Dataset 2021, the YOLOv7-MA model achieves a mean average precision (MAP) of 93.86% with a detection speed of 35.93 frames per second (FPS), outperforming Faster-RCNN, YOLOv5, YOLOX, and YOLOv7 models. Meanwhile, when tested under the three conditions of low illumination, blur, and occlusion, the coefficient of determination (R2) of YOLOv7-MA is respectively 0.9895, 0.9872, and 0.9882, and the correlation between the predicted wheat head number and the manual counting result is stronger than others. In addition, when the YOLOv7-MA model is transferred to field-collected wheat head datasets, it maintains high performance with MAP in maturity and filling stages of 93.33% and 93.03%, respectively, and R2 values of 0.9632 and 0.9155, respectively, demonstrating better performance in the maturity stage. Overall, YOLOv7-MA has achieved accurate identification and counting of wheat heads in complex field backgrounds. In the future, its application with unmanned aerial vehicles (UAVs) can provide technical support for large-scale wheat yield estimation in the field.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Meng,

Li,

et al. 2023

Remote Sensing

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“…The objective of this research is to explore a novel model capable of real-time and accurate detection of insulator defects. Noting the rapid development of YOLO models [11] in the field of general object detection task and their wide application in various industries [12,13], this paper introduces and enhances YOLOv5 model to make it adapt to the task of insulator defect detection in complex environments. Different from Faster R-CNN, YOLO innovatively proposes a single-stage object detection method, which achieves a qualitative breakthrough in computing speed and is often used for various real-time object detection tasks.…”

Section: Introductionmentioning

confidence: 99%

AE-YOLOv5 for Detection of Power Line Insulator Defects

Shen,

Fang,

Wang

et al. 2024

Preprint

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The power transmission network, which delivers power energy from generator to customers, plays an important role in the power grid. Insulator is a basic component in the power transmission network. Its defects may lead to the paralysis of the entire transmission network, resulting in serious electricity accidents. Therefore, how to use artificial intelligence and other emerging technologies to realize automatic detection of power line insulator defects has become an urgent problem to be solved. To accurately detect insulator defects in complex environment, this paper proposes Attention Enhanced YOLOv5 (AE-YOLOv5) by inserting visual attention modules into original YOLOv5 model. In particular, we design a Channel-Spatial Attention module and plug it into the backbone of YOLOv5 to enhance its representation learning ability. Furthermore, a Multi-scale Attention module is also proposed to enhance the Feature Pyramid Network (FPN). To validate the efficacy of our proposed model, we conducted training and testing on a dataset collected from real-world scenarios. The experimental results demonstrate that our model can effectively and accurately detect defects of power line insulators in real-time.

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“…This facilitates the extraction of key features from relevant areas within the image space and mitigates the issue of small object leakage. Liu et al [10] proposed the YOLO-extract algorithm, which is based on YOLOv5. The algorithm integrates coordinated attention into the network by adopting the concept of residual networks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Branch Parallel Networks for Object Detection in High-Resolution UAV Remote Sensing Images

Zhang

Guo

et al. 2023

Drones

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Uncrewed Aerial Vehicles (UAVs) are instrumental in advancing the field of remote sensing. Nevertheless, the complexity of the background and the dense distribution of objects both present considerable challenges for object detection in UAV remote sensing images. This paper proposes a Multi-Branch Parallel Network (MBPN) based on the ViTDet (Visual Transformer for Object Detection) model, which aims to improve object detection accuracy in UAV remote sensing images. Initially, the discriminative ability of the input feature map of the Feature Pyramid Network (FPN) is improved by incorporating the Receptive Field Enhancement (RFE) and Convolutional Self-Attention (CSA) modules. Subsequently, to mitigate the loss of semantic information, the sampling process of the FPN is replaced by Multi-Branch Upsampling (MBUS) and Multi-Branch Downsampling (MBDS) modules. Lastly, a Feature-Concatenating Fusion (FCF) module is employed to merge feature maps of varying levels, thereby addressing the issue of semantic misalignment. This paper evaluates the performance of the proposed model on both a custom UAV-captured WCH dataset and the publicly available NWPU VHR10 dataset. The experimental results demonstrate that the proposed model achieves an increase in APL of 2.4% and 0.7% on the WCH and NWPU VHR10 datasets, respectively, compared to the baseline model ViTDet-B.

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YOLO-Extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images

Cited by 60 publications

References 33 publications

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

AE-YOLOv5 for Detection of Power Line Insulator Defects

Multi-Branch Parallel Networks for Object Detection in High-Resolution UAV Remote Sensing Images

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