Ship detection of coast defense radar in real marine environment based on fast YOLO V4

He, Yan; Chen, Chao; Sun, Xiaohang; Li, Yibing; Geng, Zhe; Zhang, Jindong; Zhu, Daiyin

doi:10.1117/1.jrs.16.024511

Cited by 4 publications

(3 citation statements)

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“…This end-to-end network reduces the time required for filtering candidate regions and performs well in other domains, resulting in better performance. For example, Yan et al 32 devised a YOLOV4-based detection network to detect ships in real marine environments using a constructed dataset. Tu et al 33 suggested a new YOLOv3 network to recognize heavy truck blind regions in real-time for pedestrian safety.…”

Section: Yolomentioning

confidence: 99%

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

Yun

Zhao

2023

J. Electron. Imag.

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As the issue of garbage pollution becomes increasingly urgent, accurate and effective garbage detection can play a crucial role in minimizing environmental pollution and facilitating the conversion of waste into energy. However, the accuracy of current waste detection algorithms is limited. We propose an approach called the adaptive activate decoupled head for YOLOv5 (AADH-YOLOv5) for detecting garbage. Our model improves localization accuracy without significantly increasing floating point of operations (FLOPs) or parameters. Extensive testing on the newly created domestic garbage dataset-6 demonstrates that AADH-YOLOv5 outperforms YOLOv5l and Cascade R-CNN, achieving a 5.4% and 4.2% improvement in mAP@0.5, respectively. Our study also contributes to the field by providing a new dataset for garbage detection research.

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Section: Yolomentioning

confidence: 99%

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

Yun

Zhao

2023

J. Electron. Imag.

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“…Underwater target detection technology is vital not only for military maritime defense tasks [ [1] , [2] , [3] , [4] ] but also for ecological environmental protection [ 5 , 6 ], and critical economic sectors, including fisheries and aquaculture [ [7] , [8] , [9] , [10] ]. This paper focuses on enhancing target detection performance in complex underwater environments.…”

Section: Introductionmentioning

confidence: 99%

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Lian-suo,

Shen-hao,

Long-yu

2024

Heliyon

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“…The concept of lightweight refers to optimizing the model structure to achieve relatively high technical indicators under the condition of the limited number of processing units and computing power, such as on the embedded platform. 5,6 The lightweight target detection algorithm, which is suitable for embedded platforms, can achieve the same technical indicators as when it is based on desktop-level hardware under the premise of limited power consumption and computing speed; the lightweight target detection algorithm can solve the contradiction between the hardware performance of airborne embedded equipment and the demand for computing resources of the target detection algorithm, which is of great value for the theory and engineering practice of lightweight target detection algorithms based on DL. 7 Although the target detection model based on the DL can obtain relatively accurate results, it has a complex network structure and too many parameters and is unsuitable for deployment in mobile terminals.…”

Section: Introductionmentioning

confidence: 99%

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Liu,

Ma,

Wang

2023

J. Appl. Rem. Sens.

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.Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.

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Ship detection of coast defense radar in real marine environment based on fast YOLO V4

Cited by 4 publications

References 0 publications

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

MTD-YOLOv5: Enhancing marine target detection with multi-scale feature fusion in YOLOv5 model

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

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