Unmanned Aerial Vehicle Perspective Small Target Recognition Algorithm Based on Improved YOLOv5

Xu, He; Zheng, Wei; Liu, Fengxuan; Li, Peng; Wang, Ruchuan

doi:10.3390/rs15143583

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…The results show that this method can overcome the interference of sea clutter, improving the detection accuracy of small targets on the sea surface. To better suppress the interference of complex backgrounds and negative samples in images, Xu et al [ 24 ] proposed a YOLOv5s-pp algorithm, which introduces the convolutional block attention module (CBAM) to improve the feature extraction ability for small targets. The attention mechanism enables the model to autonomously learn and weight different input features by calculating the correlation between them, making the model more sensitive to important regions in the image.…”

Section: Introductionmentioning

confidence: 99%

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

Jiang,

Wu,

et al. 2023

Sensors

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As affected by limited information and the complex background, the accuracy of small-target water-floating garbage detection is low. To increase the detection accuracy, in this research, a small-target detection method based on APM-YOLOv7 (the improved YOLOv7 with ACanny PConv-ELAN and MGA attention) is proposed. Firstly, the adaptive algorithm ACanny (adaptive Canny) for river channel outline extraction is proposed to extract the river channel information from the complex background, mitigating interference of the complex background and more accurately extracting the features of small-target water-floating garbage. Secondly, the lightweight partial convolution (PConv) is introduced, and the partial convolution-efficient layer aggregation network module (PConv-ELAN) is designed in the YOLOv7 network to improve the feature extraction capability of the model from morphologically variable water-floating garbage. Finally, after analyzing the limitations of the YOLOv7 network in small-target detection, a multi-scale gated attention for adaptive weight allocation (MGA) is put forward, which highlights features of small-target garbage and decreases missed detection probability. The experimental results showed that compared with the benchmark YOLOv7, the detection accuracy in the form of the mean Average Precision (mAP) of APM-YOLOv7 was improved by 7.02%, that of mmAP (mAP0.5:0.95) was improved by 3.91%, and Recall was improved by 11.82%, all of which meet the requirements of high-precision and real-time water-floating garbage detection and provide reliable reference for the intelligent management of water-floating garbage.

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Section: Introductionmentioning

confidence: 99%

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

Jiang,

Wu,

et al. 2023

Sensors

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“…Remote sensing object detection is the process of determining the location and the category of objects in optical remote sensing images. In recent years, a large number of remote sensing object detection methods [1,2] have been proposed based on the deep learning technique. As an important and practical research field, the object detection task is not only used in the detection of ships [3], airports [4], vehicles [5] and other objects, but is also widely used in object tracking [6], instance segmentation [7], caption generation [8] and many other fields.…”

Section: Introductionmentioning

confidence: 99%

Detector Consistency Research on Remote Sensing Object Detection

Zhang,

Jin

2023

Remote Sensing

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Remote Sensing Image processing is a traditional research field, where RSI object detection is one of the most important directions. This paper focuses on an inherent problem of multi-stage object detection frameworks: the coupling error transmitting problem. In brief, because of the coupling method between the classifier and the regressor, the traditional multi-stage Detection frameworks tend to be fallible when encountering coarse object proposals. To deal with this problem, this article proposes a novel deep learning-based multi-stage object detection framework. Specifically, a novel network head architecture with a multi-to-one coupling method is proposed to avoid the coupling error of the traditional network head architecture. Moreover, it is found that the traditional network head architecture is more efficient than the novel network architecture when encountering fine object proposals. Considering this phenomenon, a proposal-consistent cooperation mechanism between the network heads is proposed. This mechanism makes the traditional network head and the novel network head develop each other’s advantages and avoid the disadvantages. Experiments with different backbone networks on three publicly available data sets have shown the effectiveness of the proposed method since mAP is proposed as 0.7% to 12.3% on most models and data sets.

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MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images

Zhou,

Zhao,

Wan

et al. 2024

Drones

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Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively.

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Unmanned Aerial Vehicle Perspective Small Target Recognition Algorithm Based on Improved YOLOv5

Cited by 5 publications

References 41 publications

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion

Detector Consistency Research on Remote Sensing Object Detection

MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images

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