scholarly journals A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test

2020 ◽  
Vol 10 (14) ◽  
pp. 4744
Author(s):  
Hyukzae Lee ◽  
Jonghee Kim ◽  
Chanho Jung ◽  
Yongchan Park ◽  
Woong Park ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
High Speed ◽  
Detection Algorithm ◽  
Learning Technologies ◽  
Experimental Conditions ◽  
Fragmentation Test ◽  
Detection Approach ◽  
Previous Image

The arena fragmentation test (AFT) is one of the tests used to design an effective warhead. Conventionally, complex and expensive measuring equipment is used for testing a warhead and measuring important factors such as the size, velocity, and the spatial distribution of fragments where the fragments penetrate steel target plates. In this paper, instead of using specific sensors and equipment, we proposed the use of a deep learning-based object detection algorithm to detect fragments in the AFT. To this end, we acquired many high-speed videos and built an AFT image dataset with bounding boxes of warhead fragments. Our method fine-tuned an existing object detection network named the Faster R-convolutional neural network (CNN) on this dataset with modification of the network’s anchor boxes. We also employed a novel temporal filtering method, which was demonstrated as an effective non-fragment filtering scheme in our recent previous image processing-based fragment detection approach, to capture only the first penetrating fragments from all detected fragments. We showed that the performance of the proposed method was comparable to that of a sensor-based system under the same experimental conditions. We also demonstrated that the use of deep learning technologies in the task of AFT significantly enhanced the performance via a quantitative comparison between our proposed method and our recent previous image processing-based method. In other words, our proposed method outperformed the previous image processing-based method. The proposed method produced outstanding results in terms of finding the exact fragment positions.

scholarly journals Ball Classification through Object Detection using Deep Learning for Handball

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Arzielah Ashiqin Alwi ◽  
Ahmad Najmuddin Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Performance Analysis ◽  
Object Detection ◽  
High Speed ◽  
Detection Accuracy ◽  
Narrow Angle ◽  
Target Locations ◽  
Ball Detection

Dynamic gameplay, fast-paced and fast-changing gameplay, where angle shooting (top and bottom corner) has the best chance of a good goal, are the main aspects of handball. When it comes to the narrow-angle area, the goalkeeper has trouble blocked the goal. Therefore, this research discusses image processing to investigate the shooting precision performance analysis to detect the ball's accuracy at high speed. In the handball goal, the participants had to complete 50 successful shots at each of the four target locations. Computer vision will then be implemented through a camera to identify the ball, followed by determining the accuracy of the ball position of floating, net tangle and farthest or smallest using object detection as the accuracy marker. The model will be trained using Deep Learning (DL)  models of YOLOv2, YOLOv3, and Faster R-CNN and the best precision models of ball detection accuracy were compared. It was found that the best performance of the accuracy of the classifier Faster R-CNN produces 99% for all ball positions.

scholarly journals U-Infuse: Democratization of Customizable Deep Learning for Object Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2611
Author(s):  
Andrew Shepley ◽  
Greg Falzon ◽  
Christopher Lawson ◽  
Paul Meek ◽  
Paul Kwan
Keyword(s):  
Deep Learning ◽  
Intellectual Property ◽  
Object Detection ◽  
Image Data ◽  
Learning Technologies ◽  
Training Data ◽  
Learning Models ◽  
Ecological Data ◽  
Single Class ◽  
Large Numbers

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

scholarly journals Using vision-based object detection for link quality prediction in 5.6-GHz channel

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Riichi Kudo ◽  
Kahoko Takahashi ◽  
Takeru Inoue ◽  
Kohei Mizuno
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Wireless Lan ◽  
Autonomous Robots ◽  
Learning Technologies ◽  
Link Quality ◽  
Automated Driving ◽  
Position Information ◽  
Prediction Scheme ◽  
Accurate Perception

Abstract Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

scholarly journals Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model

2021 ◽  
Vol 11 (2) ◽  
pp. 135-145
Author(s):  
Ying-Heng Yeo ◽  
Kin-Sam Yen
Keyword(s):  
Image Processing ◽  
Optical Properties ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Misclassification Rate ◽  
Dice Coefficient ◽  
Edible Bird’S Nest ◽  
Deep Learning Model ◽  
Output Probability ◽  
Previous Image

As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate.

scholarly journals Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2484 ◽  
Author(s):  
Weixing Zhang ◽  
Chandi Witharana ◽  
Weidong Li ◽  
Chuanrong Zhang ◽  
Xiaojiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Measurement Method ◽  
Detection Algorithm ◽  
Quality Data ◽  
Visual Interpretation ◽  
Buffer Zones ◽  
Google Street View ◽  
Street View ◽  
Utility Poles

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

scholarly journals Research on Object Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 1995 (1) ◽  
pp. 012046
Author(s):  
Meian Li ◽  
Haojie Zhu ◽  
Hao Chen ◽  
Lixia Xue ◽  
Tian Gao
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Detection Algorithm

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process

2021 ◽  
Vol 13 (24) ◽  
pp. 13834
Author(s):  
Guk-Jin Son ◽  
Dong-Hoon Kwak ◽  
Mi-Kyung Park ◽  
Young-Duk Kim ◽  
Hee-Chul Jung
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Quality Evaluation ◽  
Detection Algorithm ◽  
Synthetic Dataset ◽  
Foreign Object ◽  
Food Manufacturing ◽  
Real Dataset ◽  
The Real ◽  
Foreign Objects

Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other.

scholarly journals Identify Light-curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection

2021 ◽  
Vol 163 (1) ◽  
pp. 23
Author(s):  
Kaiming Cui ◽  
Junjie Liu ◽  
Fabo Feng ◽  
Jifeng Liu
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Human Visual Perception ◽  
Lower Confidence ◽  
Learning Techniques ◽  
Confidence Threshold ◽  
Bounding Boxes ◽  
Python Package

Abstract Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well-proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields about 90% precision and recall for identifying transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). Giving a slightly lower confidence threshold, recall can reach higher than 95%. We also transfer the trained model to the TESS data and obtain similar performance. The results of our algorithm match the intuition of the human visual perception and make it useful to find single-transiting candidates. Moreover, the parameters of the output bounding boxes can also help to find multiplanet systems. Our network and detection functions are implemented in the Deep-Transit toolkit, which is an open-source Python package hosted on Github and PyPI.

scholarly journals Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1932
Author(s):  
Malik Haris ◽  
Adam Glowacz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Single Shot ◽  
Automated Driving ◽  
Convolutional Network ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

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