Object Detection using Template and HOG Feature Matching

Sultana, Marjia; Ahmed, Tasniya; Chakraborty, Partha; Khatun, Mahmuda; Rakib, Md.; Shorif, Mohammad

doi:10.14569/ijacsa.2020.0110730

Cited by 19 publications

(2 citation statements)

References 8 publications

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“…In traditional industrial object detection, researchers often rely on manually designed feature extraction and detection algorithms. For example, methods based on features like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) have been widely used in industrial object detection tasks over the past decade [21]. However, these methods often require a large amount of manual labor and expertise and have limited detection performance in complex scenes.…”

Section: Overview Of Object Detectionmentioning

confidence: 99%

Synthetic Data Generation Based on RDB-CycleGAN for Industrial Object Detection

Hu,

Xiao,

Jin

et al. 2023

Mathematics

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Deep learning-based methods have demonstrated remarkable success in object detection tasks when abundant training data are available. However, in the industrial domain, acquiring a sufficient amount of training data has been a challenge. Currently, many synthetic datasets are created using 3D modeling software, which can simulate real-world scenarios and objects but often cannot achieve complete accuracy and realism. In this paper, we propose a synthetic data generation framework for industrial object detection tasks based on image-to-image translation. To address the issue of low image quality that can arise during the image translation process, we have replaced the original feature extraction module with the Residual Dense Block (RDB) module. We employ the RDB-CycleGAN network to transform CAD models into realistic images. Additionally, we have introduced the SSIM loss function to strengthen the network constraints of the generator and conducted a quantitative analysis of the improved RDB-CycleGAN-generated synthetic data. To evaluate the effectiveness of our proposed method, the synthetic data we generate effectively enhance the performance of object detection algorithms on real images. Compared to using CAD models directly, synthetic data adapt better to real-world scenarios and improve the model’s generalization ability.

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Section: Overview Of Object Detectionmentioning

confidence: 99%

Synthetic Data Generation Based on RDB-CycleGAN for Industrial Object Detection

Hu,

Xiao,

Jin

et al. 2023

Mathematics

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“…The detection accuracy of their suggested approach was improved by increasing the positive and negative datasets. Sultana et al [20] addressed a method to detect object in image using HOG feature and template matching technique. Where, the cross-correlation between HOG feature descriptor and the template is used.…”

Section: Introductionmentioning

confidence: 99%

Target Detection in Video Images Using HOG-Based Cascade Classifier

Namah¹,

Mirza²,

Al-Zuky³

2022

RIA

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Detecting small objects using computer vision is a challenging task due to their small size in the image and therefore the lack of features when describing them. In this paper, a computer was trained to detect three small balls using 20 levels of the AdaBoost cascade classifier. The features of the balls in each level are described using the HOG feature descriptor. Three balls were recorded in practice at various distances (d = 2, 3, 4, ..., 10 m) from the camera and the targets (balls). The frames are then taken from the videos and resized using five magnification factors (RS = 1, 3, 5, 7, and 9) to make the balls seem as they should. According to the results, the detection rate of balls at all distances was 80% when using the magnification factor RS = 1, 90% when using the magnification factor RS = 3, 5, and 7, and 100% when using the magnification factor RS = 9. The suggested approach was also used in calculating the height and width of the detected balls. The overall results indicated that the height and width of the balls dwindle as the distance between the camera and the targets increases.

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