Cascaded CNN Method for Far Object Detection in Outdoor Surveillance

Ghahremani, Amir; Bondarev, Egor

doi:10.1109/sitis.2018.00017

Cited by 6 publications

(3 citation statements)

References 10 publications

(11 reference statements)

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“…Similar approaches have been reported in [221], [222], [223]. Using SSD, [224] developed a cascade object detection method to identify obscure regions. Following some verification steps, the method considers the original high resolution input image (the one before down sampling) for decision making.…”

Section: Other Maritime Sodmentioning

confidence: 82%

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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Section: Other Maritime Sodmentioning

confidence: 82%

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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“…Lee et al (Lee et al, 2018) modifies the 10 different ship categories in the You Only Look Once (YOLO) algorithm target classification and applies them to maritime video surveillance tasks, thus enabling real-time maritime detection. Ghahremani et al (Ghahremani et al, 2018) proposed a CNN-based cascaded method for detecting maritime vessels, which takes the candidate target regions in the original image and performs additional processing to improve the accuracy of small target detection. Due to the high complexity of the cascaded method, it is not suitable for real-time monitoring applications.…”

Section: Object Detection In Maritimementioning

confidence: 99%

Multiscale attention-based detection of tiny targets in aerial beach images

et al. 2022

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Tiny target detection in marine scenes is of practical importance in marine vision applications such as personnel search and rescue, navigation safety, and marine management. In the past few years, methods based on deep convolutional neural networks (CNN) have performed well for targets of common sizes. However, the accurate detection of tiny targets in marine scene images is affected by three difficulties: perspective multiscale, tiny target pixel ratios, and complex backgrounds. We proposed the feature pyramid network model based on multiscale attention to address the problem of tiny target detection in aerial beach images with large field-of-view, which forms the basis for the tiny target recognition and counting. To improve the ability of the tiny targets’ feature extraction, the proposed model focuses on different scales of the images to the target regions based on the multiscale attention enhancement module. To improve the effectiveness of tiny targets’ feature fusion, the pyramid structure is guided by the feature fusion module in order to give further semantic information to the low-level feature maps and prevent the tiny targets from being overwhelmed by the information at the high-level. Experimental results show that the proposed model generally outperforms existing models, improves accuracy by 8.56 percent compared to the baseline model, and achieves significant performance gains on the TinyPerson dataset. The code is publicly available via Github.

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“…This method improved detection for variable object shapes and large-scale changes but fails to improve the detection accuracy small objects. Ghahremani et al [40] used the cascade CNN method to achieve high-accuracy detection of distant sea-surface objects, but did not consider the detection in dense maritime scenes. Zhang et al [35] proposed VarifocalNet on the basis of FCOS.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment

Shao

Lyu

Yin

et al. 2022

JMSE

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Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet* that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model.

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Cascaded CNN Method for Far Object Detection in Outdoor Surveillance

Cited by 6 publications

References 10 publications

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

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

Multiscale attention-based detection of tiny targets in aerial beach images

Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment

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