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

Wu, Qingyin; Zhang, Bin; Guo, Chang; Wang, Lei

doi:10.3390/drones7070439

Cited by 6 publications

(6 citation statements)

References 42 publications

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“…After the computation outlined in Equation ( 9), we obtain intermediate variables, as shown in Equation (10).…”

Section: 𝑓 = 𝛿(𝐹 ([𝑧 𝑧 ]))mentioning

confidence: 99%

“…One of the primary challenges of UAV remote sensing imaging technology lies in the inherent variability in data acquisition conditions. Unlike fixed surveillance cameras, UAVs capture images from varying altitudes, angles, and distances, leading to significant geometric distortions and scale variations in the acquired imagery [10]. These variations pose substantial challenges for pedestrian detection algorithms, which must adapt to the diverse spatial resolutions and perspectives encountered in UAV imagery.…”

Section: Introductionmentioning

confidence: 99%

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Lightweight Pedestrian Detection Network for UAV Remote Sensing Images Based on Strideless Pooling

Liu,

Cao,

2024

Remote Sensing

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The need for pedestrian target detection in uncrewed aerial vehicle (UAV) remote sensing images has become increasingly significant as the technology continues to evolve. UAVs equipped with high-resolution cameras can capture detailed imagery of various scenarios, making them ideal for monitoring and surveillance applications. Pedestrian detection is particularly crucial in scenarios such as traffic monitoring, security surveillance, and disaster response, where the safety and well-being of individuals are paramount. However, pedestrian detection in UAV remote sensing images poses several challenges. Firstly, the small size of pedestrians relative to the overall image, especially at higher altitudes, makes them difficult to detect. Secondly, the varying backgrounds and lighting conditions in remote sensing images can further complicate the task of detection. Traditional object detection methods often struggle to handle these complexities, resulting in decreased detection accuracy and increased false positives. Addressing the aforementioned concerns, this paper proposes a lightweight object detection model that integrates GhostNet and YOLOv5s. Building upon this foundation, we further introduce the SPD-Conv module to the model. With this addition, the aim is to preserve fine-grained features of the images during downsampling, thereby enhancing the model’s capability to recognize small-scale objects. Furthermore, the coordinate attention module is introduced to further improve the model’s recognition accuracy. In the proposed model, the number of parameters is successfully reduced to 4.77 M, compared with 7.01 M in YOLOv5s, representing a 32% reduction. The mean average precision (mAP) increased from 0.894 to 0.913, reflecting a 1.9% improvement. We have named the proposed model “GSC-YOLO”. This study holds significant importance in advancing the lightweighting of UAV target detection models and addressing the challenges associated with complex scene object detection.

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“…After the computation outlined in Equation ( 9), we obtain intermediate variables, as shown in Equation (10).…”

Section: 𝑓 = 𝛿(𝐹 ([𝑧 𝑧 ]))mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lightweight Pedestrian Detection Network for UAV Remote Sensing Images Based on Strideless Pooling

Liu,

Cao,

2024

Remote Sensing

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“…Anchor Frame Configuration. P2 (3,4), (6,5), (4,8), (11,6) P3 (6,12), (11,11), (10,20), (20,10) P4 (17,18), (31,16), (17,32) In the domain of small target detection in UAV imagery, a significant challenge involves effectively combining multi-scale features [20]. As shown in Figure 2, The original YOLOv5 algorithm used a cascade architecture comprising the feature pyramid network (FPN) [21] and pyramid attention network (PANet) [22] for feature fusion.…”

Section: Detection Branchmentioning

confidence: 99%

“…However, due to significant disparities in spatial and semantic information among feature maps at different levels, the fusion process easily introduced redundant information and noise, potentially leading to the loss of small object details in different levels. To address the problem of semantic disparities in feature maps at different levels, Wu et al [ 17 ], based on the use of a multi-branch parallel pyramid network, introduced a feature concatenation fusion module. Nevertheless, this method introduced a significant number of additional parameters, which consequently reduced detection speed.…”

Section: Introductionmentioning

confidence: 99%

Small Target-YOLOv5: Enhancing the Algorithm for Small Object Detection in Drone Aerial Imagery Based on YOLOv5

Zhou,

Su,

et al. 2023

Sensors

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Object detection in drone aerial imagery has been a consistent focal point of research. Aerial images present more intricate backgrounds, greater variation in object scale, and a higher occurrence of small objects compared to standard images. Consequently, conventional object detection algorithms are often unsuitable for direct application in drone scenarios. To address these challenges, this study proposes a drone object detection algorithm model based on YOLOv5, named SMT-YOLOv5 (Small Target-YOLOv5). The enhancement strategy involves improving the feature fusion network by incorporating detection layers and implementing a weighted bidirectional feature pyramid network. Additionally, the introduction of the Combine Attention and Receptive Fields Block (CARFB) receptive field feature extraction module and DyHead dynamic target detection head aims to broaden the receptive field, mitigate information loss, and enhance perceptual capabilities in spatial, scale, and task domains. Experimental validation on the VisDrone2021 dataset confirms a significant improvement in the target detection accuracy of SMT-YOLOv5. Each improvement strategy yields effective results, raising the average precision by 12.4 percentage points compared to the original method. Detection improvements for large, medium, and small targets increase by 6.9%, 9.5%, and 7.7%, respectively, compared to the original method. Similarly, applying the same improvement strategies to the low-complexity YOLOv8n results in SMT-YOLOv8n, which is comparable in complexity to SMT-YOLOv5s. The results indicate that, relative to SMT-YOLOv8n, SMT-YOLOv5s achieves a 2.5 percentage point increase in average precision. Furthermore, comparative experiments with other enhancement methods demonstrate the effectiveness of the improvement strategies.

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“…Many excellent researchers are working to solve the difficulties of UAV object detection. Wu et al [20] proposed a multi-branch parallel network that utilizes multi-branch up-sampling and down-sampling to reduce information loss when the size of a feature map changes. Wang et al [21] added an ultra-lightweight subspace attention module (ULSAM) to a path aggregation network to highlight object features.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 6 publications

References 42 publications

Lightweight Pedestrian Detection Network for UAV Remote Sensing Images Based on Strideless Pooling

Lightweight Pedestrian Detection Network for UAV Remote Sensing Images Based on Strideless Pooling

Small Target-YOLOv5: Enhancing the Algorithm for Small Object Detection in Drone Aerial Imagery Based on YOLOv5

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

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