Unsupervised Cluster Guided Object Detection in Aerial Images

Liao, Jiajia; Piao, Yun-Song; Su, Jinhe; Cai, Guorong; Huang, Xingwang; Chen, Long; Huang, Zhaohong; Wu, Yundong

doi:10.1109/jstars.2021.3122152

Cited by 27 publications

(12 citation statements)

References 27 publications

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“…Additionally, certain methods focus on resolving the issue of drastic scale variations in UAV images by incorporating multi-scale feature fusion [21,22]. Another category of methods adopts a two-stage detection strategy that guides the network to learn focused regions first and then refines the processing of these areas [23,24]. This approach effectively addresses challenges related to the detection of small objects and object clustering in UAV imagery.…”

Section: Uav Detection Methodsmentioning

confidence: 99%

Lightweight UAV Object-Detection Method Based on Efficient Multidimensional Global Feature Adaptive Fusion and Knowledge Distillation

Sun,

Gao,

Yan

et al. 2024

Electronics

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Unmanned aerial vehicles (UAVs) equipped with remote-sensing object-detection devices are increasingly employed across diverse domains. However, the detection of small, densely-packed objects against complex backgrounds and at various scales presents a formidable challenge to conventional detection algorithms, exacerbated by the computational constraints of UAV-embedded systems that necessitate a delicate balance between detection speed and accuracy. To address these issues, this paper proposes the Efficient Multidimensional Global Feature Adaptive Fusion Network (MGFAFNET), an innovative detection method for UAV platforms. The novelties of our approach are threefold: Firstly, we introduce the Dual-Branch Multidimensional Aggregation Backbone Network (DBMA), an efficient architectural innovation that captures multidimensional global spatial interactions, significantly enhancing feature distinguishability for complex and occluded targets. Simultaneously, it reduces the computational burden typically associated with processing high-resolution imagery. Secondly, we construct the Dynamic Spatial Perception Feature Fusion Network (DSPF), which is tailored specifically to accommodate the notable scale variances encountered during UAV operation. By implementing a multi-layer dynamic spatial fusion coupled with feature-refinement modules, the network adeptly minimizes informational redundancy, leading to more efficient feature representation. Finally, our novel Localized Compensation Dual-Mask Distillation (LCDD) strategy is devised to adeptly translate the rich local and global features from the higher-capacity teacher network to the more resource-constrained student network, capturing both low-level spatial details and high-level semantic cues with unprecedented efficacy. The practicability and superior performance of our MGFAFNET are corroborated by a dedicated UAV detection platform, showcasing remarkable improvements over state-of-the-art object-detection methods, as demonstrated by rigorous evaluations conducted using the VisDrone2021 benchmark and a meticulously assembled proprietary dataset.

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Section: Uav Detection Methodsmentioning

confidence: 99%

Lightweight UAV Object-Detection Method Based on Efficient Multidimensional Global Feature Adaptive Fusion and Knowledge Distillation

Sun,

Gao,

Yan

et al. 2024

Electronics

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“…Previous improvement methods for target detection in UAV aerial images can be categorized into three types: (i) utilizing more shallow feature information, such as adding small target detection layers [19]; (ii) enhancing the feature extraction capability of the target detection network, such as improving the Neck network [20] or introducing attention mechanisms [21]; and (iii) increasing input feature information, such as generating higher resolution images [22], image copying [23], and image cropping [24,25].…”

Section: Introductionmentioning

confidence: 99%

YOLOv7-UAV: An Unmanned Aerial Vehicle Image Object Detection Algorithm Based on Improved YOLOv7

Zeng

Tian

et al. 2023

Electronics

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Detecting small objects in aerial images captured by unmanned aerial vehicles (UAVs) is challenging due to their complex backgrounds and the presence of densely arranged yet sparsely distributed small targets. In this paper, we propose a real-time small object detection algorithm called YOLOv7-UAV, which is specifically designed for UAV-captured aerial images. Our approach builds upon the YOLOv7 algorithm and introduces several improvements: (i) removal of the second downsampling layer and the deepest detection head to reduce the model’s receptive field and preserve fine-grained feature information; (ii) introduction of the DpSPPF module, a spatial pyramid network that utilizes concatenated small-sized max-pooling layers and depth-wise separable convolutions to extract feature information across different scales more effectively; (iii) optimization of the K-means algorithm, leading to the development of the binary K-means anchor generation algorithm for anchor allocation; and (iv) utilization of the weighted normalized Gaussian Wasserstein distance (nwd) and intersection over union (IoU) as indicators for positive and negative sample assignments. The experimental results demonstrate that YOLOv7-UAV achieves a real-time detection speed that surpasses YOLOv7 by at least 27% while significantly reducing the number of parameters and GFLOPs to 8.3% and 73.3% of YOLOv7, respectively. Additionally, YOLOv7-UAV outperforms YOLOv7 with improvements in the mean average precision (map (0.5:0.95)) of 2.89% and 4.30% on the VisDrone2019 and TinyPerson datasets, respectively.

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“…The UGGNet model proposed by Liao et al uses a local localization LLM to predict the distribution of targets, and then generates target dense regions using an unsupervised clustering module for detection. This method saves a lot of time for detecting small targets [11]. Singh et al proposed a major improved algorithm based on YOLOv5, which can capture small details in the feature map by adding a new feature fusion layer with a small Receptive field in the feature pyramid of YOLOv5.…”

Section: Introductionmentioning

confidence: 99%

Small Target Detection Model in Aerial Images Based on YOLOv7X+

Li¹,

Liu²

2023

Preprint

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Small target detection has always been a difficult task in UAV target acquisition. The detection accuracy is low due to the high proportion of small targets, severe mutual occlusion, low feature information and the presence of complex background in the target area. To solve this problem, we propose a high-accuracy small target detection model called YOLOv7X+, which aims to improve the accuracy of UAV small target detection and strike a balance between operational efficiency. The model effectively mitigates the problem of severe mutual occlusion of small targets by introducing the Conv2Formers module for finer feature extraction of spatial information. At the same time, we propose a Bi-level routing attention mechanism based on cavity convolution, which controls feature extraction and delivery at different levels while expanding the perceptual field, enhancing the model's ability to understand and detect the connections between dense small targets through pixel-level processing, making the model more robust in detecting dense small targets in complex and variable scenes, and retaining important background information to increase discriminative accuracy. Finally, the model also is taken into account the multi-dimensional input data under the UAV by adaptively adjusting the size of the convolution kernel to make the algorithm more adaptable to the actual scene requirements. Under the validation test IOU of 0.5, the YOLOv7X+ model achieves an average mean accuracy (mAP50) of 60.3%, which is 5.4% improvement in mAP compared to the original YOLOv7X model and outperforms five current advanced detection methods. The experimental data shows that the model can effectively handle small target detection tasks in complex scenarios and achieve a balance between detection accuracy and efficiency.

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Unsupervised Cluster Guided Object Detection in Aerial Images

Cited by 27 publications

References 27 publications

Lightweight UAV Object-Detection Method Based on Efficient Multidimensional Global Feature Adaptive Fusion and Knowledge Distillation

Lightweight UAV Object-Detection Method Based on Efficient Multidimensional Global Feature Adaptive Fusion and Knowledge Distillation

YOLOv7-UAV: An Unmanned Aerial Vehicle Image Object Detection Algorithm Based on Improved YOLOv7

Small Target Detection Model in Aerial Images Based on YOLOv7X+

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