Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s

Kvietkauskas, Tautvydas; Stefanovič, Pavel

doi:10.3390/app13063761

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Based on other research [17][18][19] and our pilot studies, the results have shown that augmentation has a positive impact on detection accuracy. Furthermore, experiments have shown that by freezing backbones, the accuracy increases about 1.5 times.…”

Section: Experiments Methodologymentioning

confidence: 57%

The Influence of YOLOv5 Hyperparameters for Construction Details Detection

Kvietkauskas

2024

open-series

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Computer vision has become a fundamental area of interest in recent decades. Each area has unique data which object detection methods can analyse. However, it is important to find the most suitable parameters for the model that detects different object groups. In this research has been investigated the influence of pre-trained YOLOv5 (nano (n), small (s), medium (m), large (l), extralarge (x)) models, hyperparameters (learning rate, momentum, and weight decay) and different image augmentation (hsv_h, degrees, translate, flipud, mosaic, mixup, shear, perspective) efficiency for similar construction details detection. A newly collected dataset with twenty-two labelled categories of construction details was prepared. A total of 270 models were trained and evaluated. Every model was evaluated with 3,300 test images which backgrounds were mixed, neutral, and white backgrounds. The most accurate model was YOLOv5l with learning rate – 0.001, momentum – 0.950 and weight decay – 0.0001. This model achieved – 0.5015 (50.15%) accuracy.

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Section: Experiments Methodologymentioning

confidence: 57%

The Influence of YOLOv5 Hyperparameters for Construction Details Detection

Kvietkauskas

2024

open-series

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“…The results of our previous research [37] have shown that these parameter options allow for the highest object detection results. Image size 320, 640 (pixels) Batch size 16,32 Layers freeze option 10…”

Section: Value Of the Parameters Commentmentioning

confidence: 97%

“…In our previous research [37], the influence of training parameters on the detection of real-time construction details using YOLOv5s was analysed. Parameters, such as image resolution, batch size, iteration number, and colour of images, were investigated.…”

Section: Related Workmentioning

confidence: 99%

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

Kvietkauskas,

Pavlov,

Stefanovič

et al. 2024

Applied Sciences

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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.

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“…Given the fact that the manual inspection is labor-intensive and of low efficiency and low accuracy, in recent years, automatic surface inspection using machine vision has been applied [3][4][5][6][7][8]. Generally, the defect detection based on machine vision could be regarded as a special object detection task.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Nickel Plated Punched Steel Strip Parameters Based on Improved Circle Fitting Algorithm

Cao

Liang

et al. 2023

Electronics

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Nickel-plated punched steel strip is a product obtained by punching holes on the surface of cold-rolled white sheet steel strip and then electrochemical nickel plating. It is necessary to make accurate and fast detection of punching circle parameters, since it is of crucial importance to ensuring the quality of nickel-plated punched steel strips. Accordingly, in this article, an improved circle fitting algorithm of nickel-plated punched steel strip is proposed. Firstly, the least squares fitting is performed to obtain the circle center and radius dataset by iterative algorithm with different values for the initial point positions and intervals. Then, the mean shift algorithm is used to optimize the results after iteration, and the segmented fitted circle centers are all concentrated around the true circle center to obtain the best radius and center coordinates. Finally, comparison experiments with different numbers of circular holes and verification experiments with nickel-plated punched steel strips are carried out. As the results show, the algorithm proposed in this article is more robust than the least squares algorithm in detecting multiple circles and has better real-time performance than the Hough transform. Therefore, it can meet the industrial production needs with high accuracy and real-time requirements, such as nickel-plated punched steel strips.

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Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s

Cited by 4 publications

References 19 publications

The Influence of YOLOv5 Hyperparameters for Construction Details Detection

The Influence of YOLOv5 Hyperparameters for Construction Details Detection

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

Real-Time Detection of Nickel Plated Punched Steel Strip Parameters Based on Improved Circle Fitting Algorithm

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