Attention Scale-Aware Deformable Network for Inshore Ship Detection in Surveillance Videos

Liu, Di; Zhang, Yan; Yan, Zhao; Zhang, Yunlong

doi:10.1007/978-3-030-93046-2_50

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Various experiments in visual tasks have demonstrated the significance of fusing multiscale features. For instance, AARN [32] utilizes the attention of advanced semantic features to locate the target region, improving target region learning and suppressing interference caused by target scale changes, resulting in more accurate localization.…”

Section: Related Workmentioning

confidence: 99%

MSFA: Multi‐stage feature aggregation network for multi‐label image recognition

Chen,

Xu,

Zeng

et al. 2024

IET Image Processing

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Multi‐label image recognition (MLR) is a significant branch of image classification that aims to assign multiple categorical labels to each input. Previous research has focused on enhancing the learning of category‐related regional features. However, the potential impact of multi‐scale distributions in intra‐ and inter‐category targets on MLR tends to be neglected. Besides, semantic consistency for categories is restricted to be considered on single‐scale features, resulting in suboptimal feature extraction. To address the limitations of above, a Multi‐stage Feature Aggregation (MSFA) network is proposed. In MSFA, a novel local feature extraction method is suggested to progressively extract category‐related high‐resolution local features in both spatial and channel dimensions. Subsequently, local and global features are fused without additional up‐ and down‐sampling to enrich the scale diversity of the features while incorporating refined class‐specific information. Furthermore, a hierarchical prediction scheme for MLR is proposed, which generates classification confidence corresponding to different scales under hierarchical loss supervision. Consequently, the final output of the network comes from the joint prediction by the classifiers on multi‐scale features, ensuring a stronger feature extraction capability. The extensive experiments have been carried on VOC and MS‐COCO datasets, and the superiority of MSFA over existing mainstream methods has been verified.

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Section: Related Workmentioning

confidence: 99%

MSFA: Multi‐stage feature aggregation network for multi‐label image recognition

Chen,

Xu,

Zeng

et al. 2024

IET Image Processing

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“…The use of the attention mechanism was shown to significantly improve the detection accuracy of small ship detection. Liu et al [186] used the Convolutional Block Attention Module (CBAM) [187], which sequentially applies channel and spatial attention modules, to refine intermediate features of the object detection network. A similar attention mechanism was also used in [188], [189], [190], [191].…”

Section: Feature Learningmentioning

confidence: 99%

“…Prior works for video-based maritime small object detection are typically categorized into: (i) spatial-based (i.e., frame-based) detection and (ii) spatio-temporal based detection. The first category of methods, e.g., [168], [173], [171], [170], [199], [186], generally developed similar strategies compared to their image-based counterparts (see Section 5.1), and detected maritime small objects in videos frame by frame. While these methods (by only using the spatial information) have been able to achieve good detection accuracy and speed in several video based maritime applications, we believe that using the temporal information across video frames could lead to better performance by inferring relationships between moving objects.…”

Section: Video Based Maritime Sodmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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Attention Scale-Aware Deformable Network for Inshore Ship Detection in Surveillance Videos

Cited by 2 publications

References 19 publications

MSFA: Multi‐stage feature aggregation network for multi‐label image recognition

MSFA: Multi‐stage feature aggregation network for multi‐label image recognition

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

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