Design and implementation of object image detection interface system based on PyQt5 and improved SSD algorithm

Zhang, Hao; Huang, Weifang; Qi, Jianguang

doi:10.1109/itaic54216.2022.9836494

Cited by 6 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Alpha-EIoU loss function can expedite the regression fitting process of the real frame and prediction frame, thereby maintaining ample flexibility and robust generalization. EIoU and Alpha-EIoU are described in Equations ( 9) and (10).…”

Section: Build Loss Function Based On Alpha-eioumentioning

confidence: 99%

“…Currently, there are two main types of deep learning-based detection algorithms: onestage algorithms, such as SSD [5] and YOLO [6,7]; and two-stage algorithms, such as Mask R-CNN [8]. Two-stage algorithms have been used for detecting long-distance small targets [9], and one-stage algorithms have been used for fast identification and detection [10], as well as wire insulator fault detection and foreign-object detection [11]. Lan et al [12] proposed a new lightweight model named Swin-T YOLOX, which consists of the advanced detection network YOLOX [13] and a robust backbone Swin Transformer [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SLGA-YOLO: A Lightweight Castings Surface Defect Detection Method Based on Fusion-Enhanced Attention Mechanism and Self-Architecture

Wang,

Wang

2024

Sensors

View full text Add to dashboard Cite

Castings’ surface-defect detection is a crucial machine vision-based automation technology. This paper proposes a fusion-enhanced attention mechanism and efficient self-architecture lightweight YOLO (SLGA-YOLO) to overcome the existing target detection algorithms’ poor computational efficiency and low defect-detection accuracy. We used the SlimNeck module to improve the neck module and reduce redundant information interference. The integration of simplified attention module (SimAM) and Large Separable Kernel Attention (LSKA) fusion strengthens the attention mechanism, improving the detection performance, while significantly reducing computational complexity and memory usage. To enhance the generalization ability of the model’s feature extraction, we replaced part of the basic convolutional blocks with the self-designed GhostConvML (GCML) module, based on the addition of p2 detection. We also constructed the Alpha-EIoU loss function to accelerate model convergence. The experimental results demonstrate that the enhanced algorithm increases the average detection accuracy (mAP@0.5) by 3% and the average detection accuracy (mAP@0.5:0.95) by 1.6% in the castings’ surface defects dataset.

show abstract

Section: Build Loss Function Based On Alpha-eioumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SLGA-YOLO: A Lightweight Castings Surface Defect Detection Method Based on Fusion-Enhanced Attention Mechanism and Self-Architecture

Wang,

Wang

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In the parking lot system, the license plate recognition technology is implemented by Baidu Intelligent Cloud API [8]. Baidu Intelligent Cloud is a public cloud platform provided by Baidu, which provides intelligent big data, intelligent multimedia and intelligent Internet of Things services.…”

Section: License Plate Recognition Design and Implementationmentioning

confidence: 99%

Parking management system based on Linux online recognition

Peng

et al. 2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

View full text Add to dashboard Cite

Aiming at the problems of difficult parking, chaotic charging and low management efficiency in some small and medium sized parking lots, this paper designs a parking lot management system based on Linux online recognition. This design uses the cross-platform C++ graphical user interface library QT as the development tool. Firstly, the vehicles at the entrance and exit of the parking lot are monitored in real time through image sensors. Secondly, the license plate recognition technology is used to realize the identification of vehicles at the entrance and exit of the parking lot. In addition, this paper uses the QSqlite database to store the relevant data information of the parking lot, and finally transplants the system to the STM32 system for operation. In short, the intelligent and automatic management mode is realized, which effectively improves the operation efficiency of the parking lot.

show abstract

“…Generally, the image processing method is directly used to detect defects, and technical means such as edge enhancement are not used to construct a mask to efficiently segment and extract the ROI area. In terms of inner and outer side surface defect detection, the study of [24] fused several bearing end face images from different angles through image fusion, and the defect detection accuracy was improved to 95%. However, this method only detects the defects of the bearing's end face, and the feasibility of detecting other faces still needs to be further verified.…”

Section: Upper and Lower End Surface Defect Detectionmentioning

confidence: 99%

“…Chen et al [23] used a blue coaxial light source as the light source for the detection system to overcome the metal reflection and detect the surface of bearing dust covers. Considering the influence of the structural characteristics of the bearing on the quality of an image, Huang [24] fused multiple bearing images from different angles to solve the problem of uneven image brightness. Tu and Zou [25] used an area array camera perpendicular to the bearing's outer ring surface (the normal direction of the bearing) with a light source facing the bearing's outer ring surface, to obtain the surface image of the outer ring of the bearing and then carry out defect detection; however, this method only targets the outer surface of the bearing.…”

Section: Introductionmentioning

confidence: 99%

Whole surface defect detection method for bearing rings based on machine vision

Zhou¹,

zhang²,

Zhou³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

As an important part of a bearing, a bearing ring (BR) is prone to producing various defects on each surface in the production process, which seriously affects the reliability of the bearing. To solve the problem in which multiple defects are randomly distributed on multiple surfaces and manual detection is difficult, an automatic method for detecting defects on the whole surface of BRs based on machine vision is proposed. Firstly, the characteristics of the BR's surface defects are analyzed, and an efficient scheme for acquiring the whole surface image of the BR is designed. Then, the method for detecting the defects on the whole surface of BRs is developed, and the corresponding image preprocessing, region of interest (ROI) extraction and defect recognition algorithms are designed. Finally, a visual inspection system to identify the defects on the whole surface of BRs based on a multi-station turnover process is developed. On the premise of determining the key parameters of the detection algorithm, the performance of the detection method is analyzed through the experimental method. The results show that the comprehensive accuracy of the proposed detection method is 95%, which meets the detection requirements. On this basis, the detection strategy is optimized, and the best parameter combination is obtained through the experiments, which further reduces the false detection rate of good products and the missed detection rate of defective products, both of which are less than 3.5%.

show abstract

Design and implementation of object image detection interface system based on PyQt5 and improved SSD algorithm

Cited by 6 publications

References 11 publications

SLGA-YOLO: A Lightweight Castings Surface Defect Detection Method Based on Fusion-Enhanced Attention Mechanism and Self-Architecture

SLGA-YOLO: A Lightweight Castings Surface Defect Detection Method Based on Fusion-Enhanced Attention Mechanism and Self-Architecture

Parking management system based on Linux online recognition

Whole surface defect detection method for bearing rings based on machine vision

Contact Info

Product

Resources

About