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Electronics
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Novel Methods for Object Detection and Segmentation
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Novel Methods for Object Detection and Segmentation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 9741

Special Issue Editors

Dr. Lien Minh Dang

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
Interests: deep learning; object detection; NLP; pattern recognition; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hyeonjoon Moon

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: big data; computer vision; pattern recognition; biometrics; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Object detection and segmentation are critical tasks in computer vision with a wide range of applications, including autonomous driving, robotics, and medical imaging. In recent years, deep learning-based methods have achieved remarkable success in these areas. However, challenges remain, such as handling occlusions, low-resolution images, and diverse object shapes and sizes.

This Special Issue presents novel methods for object detection and segmentation that address these challenges. The articles cover a variety of topics, including the use of attention mechanisms, multi-scale feature fusion, and generative adversarial networks (GANs) for object detection and segmentation. Other articles explore the integration of 3D information, such as point clouds and depth maps, into object detection and segmentation frameworks.

Overall, the articles in this Special Issue offer new insights and approaches for object detection and segmentation, with potential implications for a wide range of industries and fields.

Dr. Lien Minh Dang
Prof. Dr. Hyeonjoon Moon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • object detection
  • object segmentation
  • deep learning
  • computer vision
  • attention mechanisms
  • multi-scale feature fusion
  • generative adversarial networks (GANs)
  • 3D information
  • point clouds
  • depth maps
  • autonomous driving
  • robotics
  • medical imaging
  • image processing

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Published Papers (7 papers)

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Research

26 pages, 14487 KiB  
Article
Accelerating Die Bond Quality Detection Using Lightweight Architecture DSGβSI-Yolov7-Tiny
by Bao Rong Chang, Hsiu-Fen Tsai and Wei-Shun Chang
Electronics 2024, 13(22), 4573; https://doi.org/10.3390/electronics13224573 - 20 Nov 2024
Viewed by 272
Abstract
The die bonding process is one of the most critical steps in the front-end semiconductor packaging process, as it significantly affects the yield of the entire IC packaging process. This research aims to find an efficient, intelligent vision detection model to identify whether [...] Read more.
The die bonding process is one of the most critical steps in the front-end semiconductor packaging process, as it significantly affects the yield of the entire IC packaging process. This research aims to find an efficient, intelligent vision detection model to identify whether each chip correctly adheres to the IC substrate; by utilizing the detection model to classify the type of defects occurring in the die bond images, the engineers can analyze the leading causes, enabling timely adjustments to key machine parameters in real-time, improving the yield of the die bond process, and significantly reducing manufacturing cost losses. This study proposes the lightweight Yolov7-tiny model using Depthwise-Separable and Ghost Convolutions and Sigmoid Linear Unit with β parameter (DSGβSI-Yolov7-tiny), which we can apply for real-time and efficient detection and prediction of die bond quality. The model achieves a maximum FPS of 192.3, a precision of 99.1%, and an F1-score of 0.97. Therefore, the performance of the proposed DSGβSI-Yolov7-tiny model outperforms other methods. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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22 pages, 7742 KiB  
Article
Ensemble Meta-Learning-Based Robust Chipping Prediction for Wafer Dicing
by Bao Rong Chang, Hsiu-Fen Tsai and Hsiang-Yu Mo
Electronics 2024, 13(10), 1802; https://doi.org/10.3390/electronics13101802 - 7 May 2024
Cited by 1 | Viewed by 798
Abstract
Our previous study utilized importance analysis, random forest, and Barnes–Hut t-SNE dimensionality reduction to analyze critical dicing parameters and used bidirectional long short-term memory (BLSTM) to predict wafer chipping occurrence successfully in a single dicing machine. However, each dicing machine of the same [...] Read more.
Our previous study utilized importance analysis, random forest, and Barnes–Hut t-SNE dimensionality reduction to analyze critical dicing parameters and used bidirectional long short-term memory (BLSTM) to predict wafer chipping occurrence successfully in a single dicing machine. However, each dicing machine of the same type may produce unevenly distributed non-IID dicing signals, which may lead to the undesirable result that a pre-trained model trained by dicing machine #1 could not effectively predict chipping occurrence in dicing machine #2. Therefore, regarding the model robustness, this study introduces an ensemble meta-learning-based model that can evaluate many dicing machines for chipping prediction with high stability and accuracy. This approach constructs several base learners, such as the hidden Markov model (HMM), the variational autoencoder (VAE), and BLSTM, to form an ensemble learning. We use model-agnostic meta-learning (MAML) to train and test the ensemble learning model by several prediction tasks from machine #1. After MAML learning, we call the trained model a meta learner. Then, we successfully apply a retrieved data set from machine #2 to the meta learner for training and testing wafer chipping occurrence in this machine. As a result, our contribution to the robust chipping prediction on cross-machines can improve the yield of wafer dicing with a prediction accuracy of 93.21%, preserve the practical wearing of dicing kerfs, and significantly cut wafer manufacturing costs. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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16 pages, 9203 KiB  
Article
Integration of ShuffleNet V2 and YOLOv5s Networks for a Lightweight Object Detection Model of Electric Bikes within Elevators
by Jingfang Su, Minrui Yang and Xinliang Tang
Electronics 2024, 13(2), 394; https://doi.org/10.3390/electronics13020394 - 18 Jan 2024
Viewed by 1893
Abstract
The entry of electric bikes into elevators poses safety risks. This article proposes a lightweight object detection model for edge deployment in elevator environments specifically designed for electric bikes. Based on the YOLOv5s network, the backbone network replaces the original CSPDarknet53 with a [...] Read more.
The entry of electric bikes into elevators poses safety risks. This article proposes a lightweight object detection model for edge deployment in elevator environments specifically designed for electric bikes. Based on the YOLOv5s network, the backbone network replaces the original CSPDarknet53 with a lightweight multilayer ShuffleNet V2 convolutional neural network, achieving a lightweight backbone network. Swin Transformer modules are introduced between layers to enhance the feature expression capability of images, and a SimAM attention mechanism is applied at the end layer to further improve the feature extraction capability of the backbone network. In the neck network, lightweight and depth-balanced GSConv and VoV-GSCSP modules replace several Conv and C3 basic convolutional modules, reducing the parameter count while enhancing the cross-scale connection and fusion capabilities of feature maps. The prediction network uses the faster-converging and more accurate EIOU error function as the position loss function for iterative training. This article conducts various lightweighting comparison experiments and ablation experiments on the improved object detection model. The experimental results demonstrate that the proposed object detection model, with a model size of only 2.6 megabytes and 1.1 million parameters, achieves a frame rate of 106 frames per second and a detection accuracy of 95.5%. This represents an 84.8% reduction in computational load compared to the original YOLOv5s model. The model’s volume and parameter count are reduced by 81.0% and 84.3%, respectively, with only a 0.9% decrease in mAP. The improved object detection model proposed in this paper can meet the real-time detection requirements for electric bikes in elevator scenarios, providing a feasible technical solution for its deployment on edge devices within elevators. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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14 pages, 8632 KiB  
Article
Applying Advanced Lightweight Architecture DSGSE-Yolov5 to Rapid Chip Contour Detection
by Bao Rong Chang, Hsiu-Fen Tsai and Fu-Yang Chang
Electronics 2024, 13(1), 10; https://doi.org/10.3390/electronics13010010 - 19 Dec 2023
Cited by 1 | Viewed by 1696
Abstract
Chip contour detection aims to detect damaged chips in chip slots during IC packaging and testing using vision facilities. However, the operation speed of the new chip transportation machine is too fast, and the current chip contour detection models, such as Yolov5, M3-Yolov5, [...] Read more.
Chip contour detection aims to detect damaged chips in chip slots during IC packaging and testing using vision facilities. However, the operation speed of the new chip transportation machine is too fast, and the current chip contour detection models, such as Yolov5, M3-Yolov5, FGHSE-Yolov5, and GSEH-Yolov5, running on the embedded platform, Jetson Nano, cannot detect chip contours in a timely manner. Therefore, there must be a rapid response for chip contour detection. This paper introduces the DSGSE-Yolov5s algorithm, which can accelerate object detection and image recognition to resolve this problem. Additionally, this study makes a performance comparison between the different models. Compared with the traditional model Yolov5, the proposed DSGSE-Yolov5s algorithm can significantly promote the speed of object detection by 132.17% and slightly increase the precision by 0.85%. As a result, the proposed approach can outperform the other methods. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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15 pages, 3654 KiB  
Article
Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model
by Le Quan Nguyen, Jihye Shin, Sanghuyn Ryu, L. Minh Dang, Han Yong Park, O New Lee and Hyeonjoon Moon
Electronics 2023, 12(23), 4775; https://doi.org/10.3390/electronics12234775 - 25 Nov 2023
Cited by 2 | Viewed by 1393
Abstract
Sustaining global food security amid a growing world population demands advanced breeding methods. Phenotyping, which observes and measures physical traits, is a vital component of agricultural research. However, its labor-intensive nature has long hindered progress. In response, we present an efficient phenotyping platform [...] Read more.
Sustaining global food security amid a growing world population demands advanced breeding methods. Phenotyping, which observes and measures physical traits, is a vital component of agricultural research. However, its labor-intensive nature has long hindered progress. In response, we present an efficient phenotyping platform tailored specifically for cucumbers, harnessing smartphone cameras for both cost-effectiveness and accessibility. We employ state-of-the-art computer vision models for zero-shot cucumber phenotyping and introduce a B-spline curve as a medial axis to enhance measurement accuracy. Our proposed method excels in predicting sample lengths, achieving an impressive mean absolute percentage error (MAPE) of 2.20%, without the need for extensive data labeling or model training. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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18 pages, 7701 KiB  
Article
Boosting the Response of Object Detection and Steering Angle Prediction for Self-Driving Control
by Bao Rong Chang, Hsiu-Fen Tsai and Fu-Yang Chang
Electronics 2023, 12(20), 4281; https://doi.org/10.3390/electronics12204281 - 16 Oct 2023
Cited by 1 | Viewed by 1169
Abstract
Our previous work introduced the LW-YOLOv4-tiny and the LW-ResNet18 models by replacing traditional convolution with the Ghost Conv to achieve rapid object detection and steering angle prediction, respectively. However, the entire object detection and steering angle prediction process has encountered a speed limit [...] Read more.
Our previous work introduced the LW-YOLOv4-tiny and the LW-ResNet18 models by replacing traditional convolution with the Ghost Conv to achieve rapid object detection and steering angle prediction, respectively. However, the entire object detection and steering angle prediction process has encountered a speed limit problem. Therefore, this study aims to significantly speed up the object detection and the steering angle prediction simultaneously. This paper proposes the GhostBottleneck approach to speed the frame rate of feature extraction and add the SElayer method to maintain the existing precision of object detection, which constructs an enhanced object detection model abbreviated as LWGSE-YOLOv4-tiny. In addition, this paper also conducted depthwise separable convolution to simplify the Ghost Conv as depthwise separable and ghost convolution, which constructs an improved steering angle prediction model abbreviated as LWDSG-ResNet18 that can considerably speed up the prediction and slightly increase image recognition accuracy. Compared with our previous work, the proposed approach shows that the GhostBottleneck module can significantly boost the frame rate of feature extraction by 9.98%, and SElayer can upgrade the precision of object detection slightly by 0.41%. Moreover, depthwise separable and ghost convolution can considerably boost prediction speed by 20.55% and increase image recognition accuracy by 2.05%. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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14 pages, 7299 KiB  
Article
Real Pseudo-Lidar Point Cloud Fusion for 3D Object Detection
by Xiangsuo Fan, Dachuan Xiao, Dengsheng Cai and Wentao Ding
Electronics 2023, 12(18), 3920; https://doi.org/10.3390/electronics12183920 - 18 Sep 2023
Viewed by 1664
Abstract
Three-dimensional object detection technology is an essential component of autonomous driving systems. Existing 3D object detection techniques heavily rely on expensive lidar sensors, leading to increased costs. Recently, the emergence of Pseudo-Lidar point cloud data has addressed this cost issue. However, the current [...] Read more.
Three-dimensional object detection technology is an essential component of autonomous driving systems. Existing 3D object detection techniques heavily rely on expensive lidar sensors, leading to increased costs. Recently, the emergence of Pseudo-Lidar point cloud data has addressed this cost issue. However, the current methods for generating Pseudo-Lidar point clouds are relatively crude, resulting in suboptimal detection performance. This paper proposes an improved method to generate more accurate Pseudo-Lidar point clouds. The method first enhances the stereo-matching network to improve the accuracy of Pseudo-Lidar point cloud representation. Secondly, it fuses 16-Line real lidar point cloud data to obtain more precise Real Pseudo-Lidar point cloud data. Our method achieves impressive results in the popular KITTI benchmark. Our algorithm achieves an object detection accuracy of 85.5% within a range of 30 m. Additionally, the detection accuracies for pedestrians and cyclists reach 68.6% and 61.6%, respectively. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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