A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

Hu, Kai; Lu, Feiyu; Lu, Mengji; Deng, Zhong Liang; Liu, Yunping

doi:10.1155/2020/5476142

Cited by 27 publications

(16 citation statements)

References 31 publications

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“…Experimental results demonstrate that the improved underwater vision system is capable of assisting the robot in executing underwater mission. Similar works on underwater object detection are [29], [30].…”

Section: A Object Detection Coupled With Image Enhancementmentioning

confidence: 92%

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Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²,

Zhang³

et al. 2022

Preprint

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Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the ocean in recent years. It has been widely applied in monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, and so on. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness could be degraded dramatically when conventional approaches are used. Deep learning algorithms have been found to have significant impacts on a variety of engineering fields, including marine engineering. In this context, we offer a review of deep-learning-based underwater marine object detection techniques. To facilitate a thorough understanding of the subject, we organize research challenges of underwater object detection into three categories, namely image quality degradation, small object detection, and poor generalization. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we examine the most extensively used benchmark datasets in detail and critically. Comparative studies with previous reviews, notably approaches that leverage artificial intelligence, and future trends on this hot topic are also presented. * This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

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Section: A Object Detection Coupled With Image Enhancementmentioning

confidence: 92%

“…Image enhancement as preprocess [26], [27], [28], [29], [30] Image enhancement integrated in detector [22], [31], [32], [33] Small object detection Multi-scale representation [9], [34], [35], [33], [36], [37], [18], [38],…”

Section: Image Quality Degradationmentioning

confidence: 99%

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Er¹,

Chen²,

Zhang³

et al. 2022

Preprint

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show abstract

“…Compared with semantic segmentation, target detection only obtains the object information and spatial information of the image. Furthermore, it identifies the category of each object by drawing the candidate box of the object, so target detection is faster than semantic segmentation [209]. Compared with object detection, semantic segmentation technology has higher accuracy, but its speed is much lower [210].…”

Section: Image Information Extractionmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

Self Cite

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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“…This paper describes a system of judging whether people are wearing masks that has been improved and optimized based on the SSD algorithm. The SSD algorithm combines the anchor mechanism of faster R-CNN and the regression idea of YoLo, and improves the speed and accuracy [40][41][42]. The multi-scale convolution feature map was used to predict the object region, and a series of discrete and multi-scale default frame coordinates were output.…”

Section: Face Mask Detection Modulementioning

confidence: 99%

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Fan

Yang

et al. 2021

Drones

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Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

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A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

Cited by 27 publications

References 31 publications

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

Research Challenges, Recent Advances and Benchmark Datasets in Deep-Learning-Based Underwater Marine Object Detection: A Review

An Overview on Visual SLAM: From Tradition to Semantic

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

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