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

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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A Ground-penetrating Radar Object Detection Method Based on Deep Learning

10.1109/iceict53123.2021.9531310 ◽

2021 ◽

Author(s):

Siyu Chen ◽

Li Wang ◽

Zheng Fang ◽

Zhensheng Shi ◽

Anxue Zhang

Keyword(s):

Deep Learning ◽

Object Detection ◽

Ground Penetrating Radar ◽

Detection Method ◽

Ground Penetrating

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Semi supervised inspection algorithm of automatic packaging curve based on deep learning

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-215690 ◽

2021 ◽

pp. 1-10

Author(s):

Yong He

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Optimization Algorithm ◽

Posterior Probability ◽

Detection Method ◽

Detection System ◽

Experimental Results ◽

Detection Accuracy ◽

Data Set ◽

Packaging Process

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

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A Deep Learning Method for Multiple Faults Detection and Classification of Unmanned Ground Vehicles

2019 Computer Science and Information Technologies (CSIT) ◽

10.1109/csitechnol.2019.8895161 ◽

2019 ◽

Author(s):

Jing Ren ◽

Rui Ren ◽

Mark Green ◽

Xishi Huang

Keyword(s):

Deep Learning ◽

Unmanned Ground Vehicles ◽

Learning Method ◽

Ground Vehicles ◽

Multiple Faults

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A novel enhanced region proposal network and modified loss function: threat object detection in secure screening using deep learning

The Journal of Supercomputing ◽

10.1007/s11227-020-03418-4 ◽

2020 ◽

Author(s):

Priscilla Steno ◽

Abeer Alsadoon ◽

P. W. C. Prasad ◽

Thair Al-Dala’in ◽

Omar Hisham Alsadoon

Keyword(s):

Deep Learning ◽

Object Detection ◽

Loss Function

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Ship Detection Based on YOLOv2 for SAR Imagery

Remote Sensing ◽

10.3390/rs11070786 ◽

2019 ◽

Vol 11 (7) ◽

pp. 786 ◽

Cited By ~ 41

Author(s):

Yang-Lang Chang ◽

Amare Anagaw ◽

Lena Chang ◽

Yi Wang ◽

Chih-Yu Hsiao ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Experimental Results ◽

Detection Methods ◽

Computational Time ◽

Detection Accuracy ◽

Single Shot ◽

Ship Detection ◽

Sar Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.

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Object Detection Method of Autonomous Vehicle Based on Lightweight Deep Learning

10.4271/2021-01-0192 ◽

2021 ◽

Author(s):

Rong Guo ◽

Xin Xie

Keyword(s):

Deep Learning ◽

Object Detection ◽

Detection Method ◽

Autonomous Vehicle

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A Fast Orientation Invariant Detector Based on the One-stage Method

MATEC Web of Conferences ◽

10.1051/matecconf/201823204036 ◽

2018 ◽

Vol 232 ◽

pp. 04036

Author(s):

Jun Yin ◽

Huadong Pan ◽

Hui Su ◽

Zhonggeng Liu ◽

Zhirong Peng

Keyword(s):

Object Detection ◽

Loss Function ◽

High Efficiency ◽

Detection Method ◽

State Of The Art ◽

Orientation Angle ◽

Detection Methods ◽

Detection Algorithms ◽

Bounding Boxes ◽

The One

We propose an object detection method that predicts the orientation bounding boxes (OBB) to estimate objects locations, scales and orientations based on YOLO (You Only Look Once), which is one of the top detection algorithms performing well both in accuracy and speed. Horizontal bounding boxes(HBB), which are not robust to orientation variances, are used in the existing object detection methods to detect targets. The proposed orientation invariant YOLO (OIYOLO) detector can effectively deal with the bird’s eye viewpoint images where the orientation angles of the objects are arbitrary. In order to estimate the rotated angle of objects, we design a new angle loss function. Therefore, the training of OIYOLO forces the network to learn the annotated orientation angle of objects, making OIYOLO orientation invariances. The proposed approach that predicts OBB can be applied in other detection frameworks. In additional, to evaluate the proposed OIYOLO detector, we create an UAV-DAHUA datasets that annotated with objects locations, scales and orientation angles accurately. Extensive experiments conducted on UAV-DAHUA and DOTA datasets demonstrate that OIYOLO achieves state-of-the-art detection performance with high efficiency comparing with the baseline YOLO algorithms.

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Estimation of object location probability for object detection using brightness feature only

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i6.pp5227-5234 ◽

2019 ◽

Vol 9 (6) ◽

pp. 5227

Author(s):

Hyun Jun Park ◽

Kwang Baek Kim

Keyword(s):

Object Detection ◽

Success Rate ◽

Detection Method ◽

Experimental Results ◽

New Method ◽

Object Location ◽

Detection Methods ◽

Gray Image ◽

Probability Map

Most existing object detection methods use features such as color, shape, and contour. If there are no consistent features can be used, we need a new object detection method. Therefore, in this paper, we propose a new method for estimating the probability that an object can be located for object detection and generating an object location probability map using only brightness in a gray image. To evaluate the performance of the proposed method, we applied it to gallbladder detection. Experimental results showed 98.02% success rate for gallbladder detection in ultrasonogram. Therefore, the proposed method accurately estimates the object location probability and effectively detected gallbladder.

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An Edge Computing-Enabled Train Obstacle Detection Method Based on YOLOv3

Wireless Communications and Mobile Computing ◽

10.1155/2021/7670724 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Song Li ◽

Hongli Zhao ◽

Jinmin Ma

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Detection Method ◽

Obstacle Detection ◽

Edge Computing ◽

Traffic Load ◽

Learning Tasks ◽

Onboard Equipment

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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