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Computer Vision in Automatic Detection and Identification

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 5283

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Special Issue Editors

Dr. Yongliang Qiao

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Guest Editor

Australian Centre for Field Robotics (ACFR), University of Sydney, Sydney, NSW 2006, Australia
Interests: field robotics; intelligent perception; visual localization; artificial intelligence; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Daobilige Su

E-Mail Website
Guest Editor

College of Engineering, China Agricultural University, Beijing 100083, China
Interests: field robotics; SLAM; robot audition; computer vision; machine learning
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Prof. Dr. Meili Wang

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Guest Editor

College of Information and Engineering, Northwest A&F University, Xi’an 712100, China
Interests: computer graphics; computer vision; virtual reality
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Special Issue Information

Dear Colleagues,

With recent advances, computer vision and Artificial Intelligence (AI) approaches have demonstrated great promise in Industry 4.0, smart agriculture, medicine, and other fields. The recent development and application of big data and AI approaches in particular boost computer-vision-based detection and identification. There are many theories, algorithms, and application approaches that have been proposed to solve challenges in the domains of science, engineering, and society. The purpose of this Special Issue is to report on advances and applications in computer-vision-based detection and identification. We welcome original research and review articles.

Potential topics include but are not limited to the following:

Detection and identification;
Image processing;
Object detection and segmentation;
Computer vision tools and applications;
Pattern recognition;
Digital image techniques;
Multispectral image-based detection

Dr. Yongliang Qiao
Dr. Daobilige Su
Prof. Dr. Meili Wang
Guest Editors

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Keywords

detection and identification
image processing
object detection and segmentation
computer vision tools and applications
pattern recognition
digital image techniques
multispectral image-based detection

Published Papers (7 papers)

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Research

19 pages, 5028 KiB

Open AccessArticle

ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution

by Xinghong Kuang and Bangsheng Tao

Appl. Sci. 2024, 14(11), 4653; https://doi.org/10.3390/app14114653 - 28 May 2024

Viewed by 207

Abstract

In this article, to further improve the accuracy and speed of grasp detection for unknown objects, a new omni-dimensional dynamic convolution grasp detection network (ODGNet) is proposed. The ODGNet includes two key designs. Firstly, it integrates omni-dimensional dynamic convolution to enhance the feature extraction of the graspable region. Secondly, it employs a grasping region feature enhancement fusion module to refine the features of the graspable region and promote the separation of the graspable region from the background. The ODGNet attained an accuracy of 98.4% and 97.8% on the image-wise and object-wise subsets of the Cornell dataset, respectively. Moreover, the ODGNet’s detection speed can reach 50 fps. A comparison with previous algorithms shows that the ODGNet not only improves the grasp detection accuracy, but also satisfies the requirement of real-time grasping. The grasping experiments in the simulation environment verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)

21 pages, 5253 KiB

Open AccessArticle

Using Voxelisation-Based Data Analysis Techniques for Porosity Prediction in Metal Additive Manufacturing

by Abraham George, Marco Trevisan Mota, Conor Maguire, Ciara O’Callaghan, Kevin Roche and Nikolaos Papakostas

Appl. Sci. 2024, 14(11), 4367; https://doi.org/10.3390/app14114367 - 22 May 2024

Viewed by 353

Abstract

Additive manufacturing workflows generate large amounts of data in each phase, which can be very useful for monitoring process performance and predicting the quality of the finished part if used correctly. In this paper, a framework is presented that utilises machine learning methods to predict porosity defects in printed parts. Data from process settings, in-process sensor readings, and post-process computed tomography scans are first aligned and discretised using a voxelisation approach to create a training dataset. A multi-step classification system is then proposed to classify the presence and type of porosity in a voxel, which can then be utilised to find the distribution of porosity within the build volume. Titanium parts were printed using a laser powder bed fusion system. Two discretisation techniques based on voxelisation were utilised: a defect-centric and a uniform discretisation method. Different machine learning models, feature sets, and other parameters were also tested. Promising results were achieved in identifying porous voxels; however, the accuracy of the classification requires improvement before being applied industrially. The potential of the voxelisation-based framework for this application and its ability to incorporate data from different stages of the additive manufacturing workflow as well as different machine learning models was clearly demonstrated. Full article

(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)

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20 pages, 4271 KiB

Open AccessArticle

The Efficiency of YOLOv5 Models in the Detection of Similar Construction Details

by Tautvydas Kvietkauskas, Ernest Pavlov, Pavel Stefanovič and Birutė Pliuskuvienė

Appl. Sci. 2024, 14(9), 3946; https://doi.org/10.3390/app14093946 - 6 May 2024

Viewed by 778

Abstract

Computer vision solutions have become widely used in various industries and as part of daily solutions. One task of computer vision is object detection. With the development of object detection algorithms and the growing number of various kinds of image data, different problems arise in relation to the building of models suitable for various solutions. This paper investigates the influence of parameters used in the training process involved in detecting similar kinds of objects, i.e., the hyperparameters of the algorithm and the training parameters. This experimental investigation focuses on the widely used YOLOv5 algorithm and analyses the performance of different models of YOLOv5 (n, s, m, l, x). In the research, the newly collected construction details (22 categories) dataset is used. Experiments are performed using pre-trained models of the YOLOv5. A total of 185 YOLOv5 models are trained and evaluated. All models are tested on 3300 images photographed on three different backgrounds: mixed, neutral, and white. Additionally, the best-obtained models are evaluated using 150 new images, each of which has several dozen construction details and is photographed against different backgrounds. The deep analysis of different YOLOv5 models and the hyperparameters shows the influence of various parameters when analysing the object detection of similar objects. The best model was obtained when the YOLOv5l was used and the parameters are as follows: coloured images, image size—320; batch size—32; epoch number—300; layers freeze option—10; data augmentation—on; learning rate—0.001; momentum—0.95; and weight decay—0.0007. These results may be useful for various tasks in which small and similar objects are analysed. Full article

(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)

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14 pages, 10759 KiB

Open AccessArticle

A Robust Texture-Less Gray-Scale Surface Matching Method Applied to a Liquid Crystal Display TV Diffuser Plate Assembly System

by Sicong Li, Feng Zhu and Qingxiao Wu

Appl. Sci. 2024, 14(5), 2019; https://doi.org/10.3390/app14052019 - 29 Feb 2024

Viewed by 545

Abstract

In most liquid crystal display (LCD) backlight modules (BLMs), diffuser plates (DPs) play the essential role in blurring the backlight. A common BLM consists of multiple superimposed optical films. In a vision-based automated assembly system, to ensure sufficient accuracy, each of the multiple cameras usually shoots a local corner of the DP and jointly estimates the target pose, guiding the robot to assemble the DP on the BLM. In general, DPs are typical of texture-less objects with simple shapes. Due to the image background of superimposed multilayer optical films, the robustness of the most common detection methods must be improved to meet industrial needs. To solve the above problem, a texture-less surface matching method based on gray-scale images was proposed. An augmented and normalized gray-scale vector represents the texture-less gray-scale surface in a low-dimensional space. The cosine distance is then used to calculate the similarity between the template and matching vectors, combined with shape-based matching (SBM); the proposed method can obtain high robustness when detecting DPs. An image database from actual production lines was used in the experiment. In comparative tests with the NCC, SBM, YOLOv5s, and YOLOv5x methods, our proposed method had the best precision at all confidence thresholds. Although recall was slightly inferior to SBM, the comprehensive evaluation F1-Score reached 0.826, significantly outperforming the other methods. Regarding localizing accuracy, our algorithm also performed best, reaching 5.7 pixels. Although the time consumption of a single prediction is about 0.6 s, it can still meet industrial needs. These experimental results show that the proposed method has high robustness in detecting DPs and is especially suitable for vision-based automatic assembly tasks in BLM. Full article

(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)

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29 pages, 11588 KiB

Open AccessArticle

Transmission Tower Re-Identification Algorithm Based on Machine Vision

by Lei Chen, Zuowei Yang, Fengyun Huang, Yiwei Dai, Rui Liu and Jiajia Li

Appl. Sci. 2024, 14(2), 539; https://doi.org/10.3390/app14020539 - 8 Jan 2024

Viewed by 888

Abstract

Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 ([email protected]) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%. Full article

(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)

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17 pages, 13133 KiB

Open AccessArticle

The Quality Control System of Planks Using Machine Vision

by Mariusz Cinal, Andrzej Sioma and Bartosz Lenty

Appl. Sci. 2023, 13(16), 9187; https://doi.org/10.3390/app13169187 - 12 Aug 2023

Cited by 4 | Viewed by 841

Abstract

This article presents a vision method of identifying and measuring wood surface parameters to detect defects resulting from errors occurring during machining. The paper presents the method of recording a three–dimensional image of the wood surface using the laser triangulation method. It discusses parameters related to imaging resolution and the impact of vision system configuration parameters on the measurement resolution and image acquisition time. For the recorded image, proposed algorithms detect defects like wade and bark at the board edges. Algorithms for measuring characteristic parameters describing the surface of the wood are presented. Validation tests performed using the prepared system in industrial conditions are provided and discussed. The proposed solution makes it possible to detect board defects in flow mode on belt conveyors operating at a speed of up to 1000 mm/s. Full article

(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)

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20 pages, 16693 KiB

Open AccessArticle

Rapid and Accurate Crayfish Sorting by Size and Maturity Based on Improved YOLOv5

by Xuhui Ye, Yuxiang Liu, Daode Zhang, Xinyu Hu, Zhuang He and Yan Chen

Appl. Sci. 2023, 13(15), 8619; https://doi.org/10.3390/app13158619 - 26 Jul 2023

Cited by 3 | Viewed by 993

Abstract

In response to the issues of high-intensity labor, low efficiency, and potential damage to crayfish associated with traditional manual sorting methods, an automated and non-contact sorting approach based on an improved YOLOv5 algorithm is proposed for the rapid sorting of crayfish maturity and size. To address the difficulty in focusing on small crayfish, the Backbone is augmented with Coordinate Attention to boost its capability to extract features. Additionally, to address the difficulty in achieving high overall algorithm efficiency and reducing feature redundancy, the Bottleneck Transformer is integrated into both the Backbone and Neck, which improves the accuracy, generalization performance, and the model’s computational proficiency. The dataset of 3464 images of crayfish collected from a crayfish breeding farm is used for the experiments. The dataset is partitioned randomly, with 80% of the data used for training and the remaining 20% used for testing. The results indicate that the proposed algorithm achieves an mAP of 98.8%. Finally, the model is deployed using TensorRT, and the processing time for an image is reduced to just 2 ms, which greatly improves the processing speed of the model. In conclusion, this approach provides an accurate, efficient, fast, and automated solution for crayfish sorting. Full article

(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification)

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