Detection algorithm for dense small objects in high altitude image

Wu, Mingjie; Yun, Lijun; Wang, Yibo; Chen, Zaiqing; Cheng, Feiyan

doi:10.1016/j.dsp.2024.104390

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…One of the drawbacks of the detection head in the YOLO algorithm is that, since the detection head typically operates at the final layer of the network, it may miss some low-level detailed information. This can result in lower detection accuracy, particularly for small objects or in complex scenes [48,49]. Therefore, we redesigned the detection head of YOLO and thoroughly analyzed the performance of the detection head in w-YOLO.…”

Section: Detection Layer Analysismentioning

confidence: 99%

Walnut Recognition Method for UAV Remote Sensing Images

Wu,

Yun,

Xue

et al. 2024

Agriculture

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During the process of walnut identification and counting using UAVs in hilly areas, the complex lighting conditions on the surface of walnuts somewhat affect the detection effectiveness of deep learning models. To address this issue, we proposed a lightweight walnut small object recognition method called w-YOLO. We reconstructed the feature extraction network and feature fusion network of the model to reduce the volume and complexity of the model. Additionally, to improve the recognition accuracy of walnut objects under complex lighting conditions, we adopted an attention mechanism detection layer and redesigned a set of detection heads more suitable for walnut small objects. A series of experiments showed that when identifying walnut objects in UAV remote sensing images, w-YOLO outperforms other mainstream object detection models, achieving a mean Average Precision (mAP0.5) of 97% and an F1-score of 92%, with parameters reduced by 52.3% compared to the YOLOv8s model. Effectively addressed the identification of walnut targets in Yunnan, China, under the influence of complex lighting conditions.

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Section: Detection Layer Analysismentioning

confidence: 99%

Walnut Recognition Method for UAV Remote Sensing Images

Wu,

Yun,

Xue

et al. 2024

Agriculture

Self Cite

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“…This section will further compare HeMoDU with current typical methods on the Visdrone2019 dataset and the UA-DETRAC dataset to verify its superior performance. We compare HeMoDU with YOLOv3 [19], YOLOv4 [20], Li et al [35], DFE-Net [36], EM-YOLO [37], YOLOv8s [34], and EdgeYOLO [38] on the Visdrone2019 dataset and with YOLOv5s, YOLOv5-NAM [39], Peng et al [40], and YOLOv8s on the UA-DETRAC dataset.…”

Section: Comparative Experimentsmentioning

confidence: 99%

HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads

Shi,

Wang,

et al. 2024

Sensors

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Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.

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High-precision and lightweight small-target detection algorithm for low-cost edge intelligence

Xiao,

Li,

Yao

et al. 2024

Sci Rep

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The proliferation of edge devices driven by advancements in Internet of Things (IoT) technology has intensified the challenge of achieving high-precision small target detection, as it demands extensive computational resources. This amplifies the conflict between the need for precise detection and the requirement for cost-efficiency across numerous edge devices. To solve this problem, this paper introduces an enhanced target detection algorithm, MSGD-YOLO, built upon YOLOv8. The Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module is enhanced through the integration of the Ghost module and dynamic convolution, resulting in a more lightweight architecture while enhancing feature generation. Additionally, Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) replaces Spatial Pyramid Pooling Fast (SPPF) to expand the receptive field, optimizing multi-level feature aggregation for improved performance. Furthermore, a novel Multi-Scale Ghost Convolution (MSGConv) and Multi-Scale Generalized Feature Pyramid Network (MSGPFN) are introduced to enhance feature fusion and integrate multi-scale information. Finally, four optimized dynamic convolutional detection heads are employed to capture target features more accurately and improve small target detection precision. Evaluation on the VisDrone2019 dataset shows that compared with YOLOv8-n, MSGD-YOLO improves mAP@50 and mAP@50–95 by 14.1% and 11.2%, respectively. In addition, the model not only achieves a 16.1% reduction in parameters but also attains a processing speed of 24.6 Frames Per Second (FPS) on embedded devices, thereby fulfilling real-time detection requirements.

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Detection algorithm for dense small objects in high altitude image

Cited by 5 publications

References 32 publications

Walnut Recognition Method for UAV Remote Sensing Images

Walnut Recognition Method for UAV Remote Sensing Images

HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads

High-precision and lightweight small-target detection algorithm for low-cost edge intelligence

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