Remote sensing image fine-processing method based on the adaptive hyper-Laplacian prior

Jiang, Shikai; Zhi, Xu; Zhang, Wei; Wang, Dawei; Hu, Jianming; Chen, Wenbin

doi:10.1016/j.optlaseng.2020.106311

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…Aiming at the problem of object detection, many scholars have proposed a variety of processing strategies based on the characteristics of remote sensing images [13,14]. In addition, varied methods based on deep network have been presented in recent years and achieved excellent detection performance [15,16].…”

Section: Previous Related Researchmentioning

confidence: 99%

PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

Zhi

Shi

et al. 2021

Remote Sensing

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The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.

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Section: Previous Related Researchmentioning

confidence: 99%

PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

Zhi

Shi

et al. 2021

Remote Sensing

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“…Object detection is one of the core problems in the field of computer vision research [1][2][3], where the task is to find all the objects of interest in an image and determine their class and location [4,5]. Object detection is also abundant and promising in real life-for example, in remote sensing image processing [6][7][8][9], UAV navigation [10][11][12], autonomous driving [13][14][15], medical diagnosis [16,17], face recognition [18,19], and many other fields [20,21].…”

Section: Introductionmentioning

confidence: 99%

DA-FPN: Deformable Convolution and Feature Alignment for Object Detection

Yuan

et al. 2023

Electronics

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This study sought to address the problem of the insufficient extraction of shallow object information and boundary information when using traditional FPN structures in current object detection algorithms, which degrades object detection accuracy. In this paper, a new FPN structure model, DA-FPN, is proposed. DA-FPN replaces the 1 × 1 convolution used in the conventional FPN structure for lateral connection with a 3 × 3 deformable convolution and adds a feature alignment module after the 2x downsampling operation used for lateral connection. This design allows the detection framework to extract more accurate information about the boundary of the object, particularly the boundary information of small objects. A bottom-up module was also added to incorporate the shallow information of the object more accurately into the high-level feature map, and a feature alignment module was added to the bottom-up module, thereby improving object detection accuracy. The experimental results show that DA-FPN can improve the accuracy of the single-stage object detection algorithms FoveaBox and GFL by 1.7% and 2.4%, respectively, on the MS-COCO dataset. This model was also found to improve the two-stage object detection algorithm SABL by 2.4% and offer higher small object detection accuracy and better robustness.

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