Water surface object detection using panoramic vision based on improved single-shot multibox detector

Li, Aofeng; Zhu, Xiaoxia; He, Shuo; Xia, Jiawei

doi:10.1186/s13634-021-00831-6

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…In parallel, another study by Zhang et al [12] integrated multi-level features to refine the faster R-CNN model, thereby amplifying the velocity of water surface detection tasks. Li et al [44] introduced enhancements to the SSD algorithm, effectively addressing the challenge of non-detection zones that commonly plague water surface target detection scenarios. Ma et al [45] present an innovative approach by integrating CLIP into the realm of water surface garbage object detection, advocating a method that synergizes both strong and weak supervisory signals.…”

Section: Water Surface Object Detectionmentioning

confidence: 99%

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

Zhao,

Qiu,

Chen

2024

Sensors

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The detection of objects on water surfaces is a pivotal technology for the perceptual systems of unmanned surface vehicles (USVs). This paper proposes a novel real-time target detection system designed to address the challenges posed by indistinct bottom boundaries and foggy imagery. Our method enhances the YOLOv8s model by incorporating the convolutional block attention module (CBAM) and a self-attention mechanism, examining their impact at various integration points. A dynamic sample assignment strategy was introduced to enhance the precision of our model and accelerate its convergence. To address the challenge of delineating bottom boundaries with clarity, our model employs a two-strategy approach: a threshold filter and a feedforward neural network (FFN) that provides targeted guidance for refining these boundaries. Our model demonstrated exceptional performance, achieving a mean average precision (mAP) of 47.1% on the water surface object dataset, which represents a 1.7% increase over the baseline YOLOv8 model. The dynamic sample assignment strategy contributes a 1.0% improvement on average precision at the intersection over union (IoU) threshold of 0.5 (AP0.5), while the FFN strategy fine-tunes the bottom boundaries and achieves an additional 0.8% improvement in average precision at IoU threshold of 0.75 (AP0.75). Furthermore, ablation studies have validated the versatility of our approach, confirming its potential for integration into various detection frameworks.

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Section: Water Surface Object Detectionmentioning

confidence: 99%

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

Zhao,

Qiu,

Chen

2024

Sensors

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“…As follow-up research has deepened, the backbone network of SSD has also improved, mainly including ResNet [99], DenseNet [100], and MobileNet [101]. For example, Li et al [102] proposed an improved SSD that uses ResNet-50 to replace VGG-16 for water surface target detection. ResNet-50 increased the network depth compared to VGG and could solve the problem of gradient disappearance by shortcut, enabling the network to obtain more semantic information.…”

Section: Single Shot Multi-box Detector (Ssd)mentioning

confidence: 99%

“…For example, Li et al. [102] proposed an improved SSD that uses ResNet‐50 to replace VGG‐16 for water surface target detection. ResNet‐50 increased the network depth compared to VGG and could solve the problem of gradient disappearance by shortcut, enabling the network to obtain more semantic information.…”

Section: Deep Learning For Object Detectionmentioning

confidence: 99%

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

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The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture.

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“…Zhang et al [178] improved Faster R-CNN by fusing low-and high-level features to generate object proposals, predict bounding boxes and classification scores for float detection. Li et al [179] integrated feature maps from a number of layers by employing a feature pyramid network structure with deconvolutions into SSD, effectively improving the detection performance of remote objects in water surface. Additionally, the fusion of shallow features and deep features has also been used to detect ships in remote sensing images [160] for ship detection of remote sensing images.…”

Section: Feature Learningmentioning

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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Water surface object detection using panoramic vision based on improved single-shot multibox detector

Cited by 8 publications

References 25 publications

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

Enhanced Water Surface Object Detection with Dynamic Task-Aligned Sample Assignment and Attention Mechanisms

A review of intelligent ship marine object detection based on RGB camera

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

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