Object detection method for ship safety plans using deep learning

Rail transit is developing towards intelligence which takes lots of computation resource to perform deep learning tasks. Among these tasks, object detection is the most widely used, like track obstacle detection, catenary wear, and defect detection and looseness detection of train wheel bolts. But the limited computation capability of the train onboard equipment prevents running deep and complex detection networks. The limited computation capability of the train onboard equipment prevents conducting complex deep learning tasks. Cloud computing is widely utilized to make up for the insufficient onboard computation capability. However, the traditional cloud computing architecture will bring in uncertain heavy traffic load and cause high transmission delay, which makes it fail to complete real-time computing intensive tasks. As an extension of cloud computing, edge computing (EC) can reduce the pressure of cloud nodes by offloading workloads to edge nodes. In this paper, we propose an edge computing-based method. The onboard equipment on a fast-moving train is responsible for acquiring real-time images and completing a small part of the inference task. Edge computing is used to help execute the object detection algorithm on the trackside and carry most of the computing power. YOLOv3 is selected as the object detection model, since it can balance between the real-time and accurate performance on object detection compared with two-stage models. To save onboard equipment computation resources and realize the edge-train cooperative interface, we propose a model segmentation method based on the existing YOLOv3 model. We implement the cooperative inference scheme in real experiments and find that the proposed EC-based object detection method can accomplish real-time object detection tasks with little onboard computation resources.

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SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing

Sensors ◽

10.3390/s20143918 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3918

Author(s):

Noi Quang Truong ◽

Young Won Lee ◽

Muhammad Owais ◽

Dat Tien Nguyen ◽

Ganbayar Batchuluun ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Processing Time ◽

Detection Method ◽

Uniform Motion ◽

Two Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Motion Deblurring ◽

Marker Detection

Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.

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DETECTION OF ARTIFICIAL OBJECTS IN REMOTE SENSING IMAGE BASED ON DEEP LEARNING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-321-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 321-326 ◽

Cited By ~ 1

Author(s):

Y. Dai ◽

J. S. Xiao ◽

B. S. Yi ◽

J. F. Lei ◽

Z. Y. Du

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Object Detection ◽

Detection Method ◽

Remote Sensing Image ◽

Remote Sensing Images ◽

Feature Maps ◽

Aspect Ratios ◽

Efficient Detection

Abstract. Aiming at multi-class artificial object detection in remote sensing images, the detection framework based on deep learning is used to extract and localize the numerous targets existing in very high resolution remote sensing images. In order to realize rapid and efficient detection of the typical artificial targets on the remote sensing image, this paper proposes an end-to-end multi-category object detection method in remote sensing image based on the convolutional neural network to solve several challenges, including dense objects and objects with arbitrary direction and large aspect ratios. Specifically, in this paper, the feature extraction process is improved by utilizing a more advanced backbone network with deeper layers and combining multiple feature maps including the high-resolution features maps with more location details and low-resolution feature maps with highly-abstracted information. And a Rotating Regional Proposal Network is adopted into the Faster R-CNN network to generate candidate object-like regions with different orientations and to improve the sensitivity to dense and cluttered objects. The rotation factor is added into the regional proposal network to control the generation of anchor box’s angle and to cover enough directions of typical man-made objects. Meanwhile, the misalignment caused by the two quantifications operations in the pooling process is eliminated and a convolution layer is appended before the fully connected layer of the final classification network to reduce the feature parameters and avoid overfitting. Compared with current generic object detection method, the proposed algorithm focus on the arbitrary oriented and dense artificial targets in remote sensing images. After comprehensive evaluation with several state-of-the-art object detection algorithms, our method is proved to be effective to detect multi-class artificial object in remote sensing image. Experiments demonstrate that the proposed method combines the powerful features extracted by the improved convolutional neural networks with multi-scale features and rotating region network is more accurate in the public DOTA dataset.

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A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Complexity ◽

10.1155/2021/5518196 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Linghua Zhao ◽

Zhihua Huang

Keyword(s):

Wireless Sensor Networks ◽

Deep Learning ◽

Sensor Networks ◽

Object Detection ◽

Moving Objects ◽

Detection Method ◽

Learning Algorithm ◽

Wireless Sensor ◽

Moving Target ◽

Deep Learning Algorithm

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.

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Self-Recurrent Learning and Gap Sample Feature Synthesis-Based Object Detection Method

Mathematical Problems in Engineering ◽

10.1155/2021/2920062 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Lvjiyuan Jiang ◽

Haifeng Wang ◽

Kai Yan ◽

Chengjiang Zhou ◽

Songlin Li ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Production Efficiency ◽

Detection Method ◽

Feature Fusion ◽

Information Filtering ◽

Construction Work ◽

Convolutional Network ◽

Features Fusion ◽

Feature Augmentation

Object detection-based deep learning by using the looking and thinking twice mechanism plays an important role in electrical construction work. Nevertheless, the use of this mechanism in object detection produces some problems, such as calculation pressure caused by multilayer convolution and redundant features that confuse the network. In this paper, we propose a self-recurrent learning and gap sample feature fusion-based object detection method to solve the aforementioned problems. The network consists of three modules: self-recurrent learning-based feature fusion (SLFF), residual enhancement architecture-based multichannel (REAML), and gap sample-based features fusion (GSFF). SLFF detects objects in the background through an iterative convolutional network. REAML, which serves as an information filtering module, is used to reduce the interference of redundant features in the background. GSFF adds feature augmentation to the network. Simultaneously, our model can effectively improve the operation and production efficiency of electric power companies’ personnel and guarantee the safety of lives and properties.

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Deep Learning based Object Detection Method and its Application for Intelligent Transport Systems

Journal of Institute of Control Robotics and Systems ◽

10.5302/j.icros.2021.21.0145 ◽

2021 ◽

Vol 27 (12) ◽

pp. 1016-1022

Author(s):

Jun-yong Kim ◽

Seong-heum Kim

Keyword(s):

Deep Learning ◽

Object Detection ◽

Detection Method ◽

Transport Systems ◽

Intelligent Transport Systems ◽

Intelligent Transport

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A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles

PLoS ONE ◽

10.1371/journal.pone.0251339 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0251339

Author(s):

Qian Xu ◽

Gang Wang ◽

Ying Li ◽

Ling Shi ◽

Yaxin Li

Keyword(s):

Deep Learning ◽

Object Detection ◽

Loss Function ◽

Detection Method ◽

Experimental Results ◽

Svm Classifier ◽

Grey Wolf Optimizer ◽

Unmanned Ground Vehicles ◽

Ground Vehicles ◽

Research Application

Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.

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