Research of Maritime Object Detection Method in Foggy Environment Based on Improved Model SRC-YOLO

Zhang, Yihong; Ge, Hang; Lin, Qin; Zhang, Ming; Sun, Qiantao

doi:10.3390/s22207786

Cited by 6 publications

(3 citation statements)

References 36 publications

(34 reference statements)

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“…In scenarios such as autonomous navigation, the presence of haze can severely impact object detection, leading to degraded image quality and potential safety risks. Therefore, it becomes imperative to employ a preprocessing step for image enhancement before undertaking tasks like object detection [126][127][128][129][130].…”

Section: Results On Real-world Hazy Imagesmentioning

confidence: 99%

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Liu,

Deng,

Shao

2024

CMES

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The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach materializes as an endto-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump connections. The essence of our methodology lies in local feature fusion within dense residual clusters, enabling the extraction of pertinent features from both preceding and current local data, depending on contextual demands. The intelligently orchestrated gated structures facilitate the propagation of these features to the decoder, resulting in superior outcomes in haze removal. Empirical validation through a plethora of experiments substantiates the efficacy of URA-Net, demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image defogging. On the RICE-1 dataset, URA-Net achieves a Peak Signal-to-Noise Ratio (PSNR) of 29.07 dB, surpassing the Dark Channel Prior (DCP) by 11.17 dB, the All-in-One Network for Dehazing (AOD) by 7.82 dB, the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing (OTM-AAL) by 5.37 dB, the Unsupervised Single Image Dehazing (USID) by 8.0 dB, and the Superpixelbased Remote Sensing Image Dehazing (SRD) by 8.5 dB. Particularly noteworthy, on the SateHaze1k dataset, URA-Net attains preeminence in overall performance, yielding defogged images characterized by consistent visual quality. This underscores the contribution of the research to the advancement of remote sensing technology, providing a robust and efficient solution for alleviating the adverse effects of haze on image quality.

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Section: Results On Real-world Hazy Imagesmentioning

confidence: 99%

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Liu,

Deng,

Shao

2024

CMES

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“…It has developed from the deepening of the VGG network structure to the expanding initial network [27][28][29] and Resnet [30,31] network, then to the lightweight mobile network [32] and advanced network [33]. The focus of CNN research has also shifted from parameter optimization to designing network architectures, such as using a new architecture based on the attention mechanism [34][35][36] to improve the relevant performance of networks. Deep learning-based object detection can be divided into two categories: two-stage detection and one-stage detection.…”

Section: Object Detectionmentioning

confidence: 99%

Multi-Scene Mask Detection Based on Multi-Scale Residual and Complementary Attention Mechanism

Zhou,

Lin,

Luo

et al. 2023

Sensors

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Vast amounts of monitoring data can be obtained through various optical sensors, and mask detection based on deep learning integrates neural science into a variety of applications in everyday life. However, mask detection poses technical challenges such as small targets, complex scenes, and occlusions, which necessitate high accuracy and robustness in multi-scene target detection networks. Considering that multi-scale features can increase the receptive field and attention mechanism can improve the detection effect of small targets, we propose the YOLO-MSM network based on the multi-scale residual (MSR) block, multi-scale residual cascaded channel-spatial attention (MSR-CCSA) block, enhanced residual CCSA (ER-CCSA) block, and enhanced residual PCSA (ER-PCSA) block. Considering the performance and parameters, we use YOLOv5 as the baseline network. Firstly, for the MSR block, we construct hierarchical residual connections in the residual blocks to extract multi-scale features and obtain finer features. Secondly, to realize the joint attention function of channel and space, both the CCSA block and PCSA block are adopted. In addition, we construct a new dataset named Multi-Scene-Mask, which contains various scenes, crowd densities, and mask types. Experiments on the dataset show that YOLO-MSM achieves an average precision of 97.51%, showing better performance than other detection networks. Compared with the baseline network, the mAP value of YOLO-MSM is increased by 3.46%. Moreover, we propose a module generalization improvement strategy (GIS) by training YOLO-MSM on the dataset augmented with white Gaussian addition noise to improve the generalization ability of the network. The test results verify that GIS can greatly improve the generalization of the network and YOLO-MSM has stronger generalization ability than the baseline.

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“…Weather factors can significantly impact ISMOD [122, 123]. Strong sunshine can generate vital light reflection areas on the sea surface, resulting in unclear object information.…”

Section: Challenges and Trendsmentioning

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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Research of Maritime Object Detection Method in Foggy Environment Based on Improved Model SRC-YOLO

Cited by 6 publications

References 36 publications

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

Multi-Scene Mask Detection Based on Multi-Scale Residual and Complementary Attention Mechanism

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

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