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Notghimoghadam, S.M., Farsi, H., Mohamadzadeh, S.. (1401). Object Detection by a Hybrid of Feature Pyramid and Deep Neural Networks. فناوری آموزش, 11(1), 173-182. doi: 10.22061/jecei.2022.9012.567
S.M. Notghimoghadam; H. Farsi; S. Mohamadzadeh. "Object Detection by a Hybrid of Feature Pyramid and Deep Neural Networks". فناوری آموزش, 11, 1, 1401, 173-182. doi: 10.22061/jecei.2022.9012.567
Notghimoghadam, S.M., Farsi, H., Mohamadzadeh, S.. (1401). 'Object Detection by a Hybrid of Feature Pyramid and Deep Neural Networks', فناوری آموزش, 11(1), pp. 173-182. doi: 10.22061/jecei.2022.9012.567
Notghimoghadam, S.M., Farsi, H., Mohamadzadeh, S.. Object Detection by a Hybrid of Feature Pyramid and Deep Neural Networks. فناوری آموزش, 1401; 11(1): 173-182. doi: 10.22061/jecei.2022.9012.567

Object Detection by a Hybrid of Feature Pyramid and Deep Neural Networks

Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 11، شماره 1، فروردین 2023، صفحه 173-182    اصل مقاله (840.71 K)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2022.9012.567
نویسندگان
S.M. Notghimoghadam1؛ H. Farsi* 2؛ S. Mohamadzadeh1
1Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
2Department of Communication Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 15 اردیبهشت 1401،  تاریخ بازنگری: 31 تیر 1401،  تاریخ پذیرش: 08 شهریور 1401 
چکیده
Background and Objectives: Object detection has been a fundamental issue in computer vision. Research findings indicate that object detection aided by convolutional neural networks (CNNs)‌ is still in its infancy despite -having outpaced‌ other methods‌.
Methods: This study proposes a straightforward, easily implementable, and high-precision object detection method that can detect objects ‌‌‌with minimum least error. ‌Object detectors generally fall into one-stage ‌‌and two-stage‌‌ detectors‌‌. Unlike one-stage detectors, two-stage detectors ‌are often more precise, despite performing at a lower speed. In this study, a one-stage‌‌ detector is proposed, and the results indicated its sufficient precision‌. The proposed method uses a feature pyramid network ‌(FPN) to detect objects on multiple scales‌. This network is combined with the ResNet 50 deep neural network.
Results: The proposed method is trained and tested on ‌Pascal VOC 2007 and COCO datasets. It yields a mean average precision (mAP) of 41.91 in Pascal Voc2007 and 60.07% in MS COCO. The proposed method is tested under additive noise. The test images of the datasets are combined with the salt and pepper noise to obtain the value of mAP for different noise levels up to 50% for Pascal VOC and MS COCO datasets. The investigations show that the proposed method provides acceptable results.
Conclusion: It can be concluded that using deep learning algorithms‌ and CNNs‌ and combining them with a feature network can significantly enhance object detection precision.
کلیدواژه‌ها
Object Recognition؛ Deep Learning؛ Convolutional Neural Networks؛ Object Classification
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