Object Identification and Tracking Using YOLO Model: A CNN-Based Approach

Singh, Shweta; Suri, Ajay; Singh, J. N.; Singh, Muskan; Nikita,; Yadav, Dileep Kumar

doi:10.1007/978-981-33-4859-2_15

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…CNN can be used to split images into different formats. Each pixel is classified based on the object to which it belongs [17], which allows us to understand the content of the image.…”

Section: Image Segmentationmentioning

confidence: 99%

Image Enhancement using Convolution Neural Networks

Salman,

Kalakech

2024

Babylonian Journal of Machine Learning

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The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN).The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with images incrementally and extracting different attributes in each layer. The research also highlights CNN's ability to detect, learn and improve important details found in images through convolutions, filtering and data aggregation processes. The proposed CNN image enhancement model was developed and tested on both medical and normal images. The images were optimized using the proposed model and compared with other models. Various quality measures were used to evaluate the results. The results showed that the proposed model can significantly improve the quality of images.

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“…CNN can be used to split images into different formats. Each pixel is classified based on the object to which it belongs [17], which allows us to understand the content of the image.…”

Section: Image Segmentationmentioning

confidence: 99%

Image Enhancement using Convolution Neural Networks

Salman,

Kalakech

2024

Babylonian Journal of Machine Learning

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“…The image is first given to CSPDarknet53 for feature extraction. The backbone network generates feature maps of different sizes from the input image [34][35][36][37][38][39]. PANet's neck network integrates feature maps from multiple levels with feature maps of various sizes to obtain more contextual information and reduce data loss.…”

Section: Architecture Of Yolov5mentioning

confidence: 99%

Robust Vehicle Detection Based on Improved You Look Only Once

Kumar¹,

Jailia²,

Varshney³

et al. 2023

Computers, Materials &Amp; Continua

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Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets to generate the classes of the objects. Subsequently, in the second phase, the YOLOv5 is applied to create the bounding box, and the Non-Maximum Suppression (NMS) technique is used to eliminate the overlapping of the bounding boxes of the vehicle. Then, the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase. The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques, namely Lightweight Dilated Convolutional Neural Network (LD-CNN), Single Shot Detector (SSD), YOLOv3 and YOLOv4 on the performance metric like precision, recall, mAP and F1-Score. The simulation and analysis are carried out on PASCAL VOC 2007, 2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection. Finally, the RVD-YOLOv5 obtains the results with an mAP of 98.6% and Precision, Recall, and F1-Score are 98%, 96.2% and 97.09%, respectively.

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“…Şekil 3. Nesne Denetimi [23] İHA içerisinde yer alan gömülü sistemler, yapay sinir ağlarını gerçek zamanlı çalıştırmaktadır. Buna bağlı olarak derin öğrenme yöntemlerinden hızlı sonuçlar alınmaktadır.…”

Section: Evrişimli Sinir Ağlarıunclassified

İnsansız Hava Araçlarında Gömülü Sistem Üzerinden Derin Öğrenme ile Nesne Tespiti

SAYGILI¹,

Özmen²

2022

European Journal of Science and Technology

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ÖzHava robotiği olarak adlandırılan insansız hava araçları (İHA), afet yönetimi, trafik yoğunluğu ve sınır güvenliği gibi sivil ve askeri alanlarda veri ve görüntü toplamak için son zamanlarda sıklıkla kullanılmaktadır. Kamera görüş açısının değişkenliğinden dolayı İHA ile yüksek irtifada alınan görüntüler üzerinden anlık nesne tespiti yapmanın zorlukları vardır. Bu çalışma İHA' ya CSI (Camera Serial Interface) modülü ile bağlanmış bir kameradan, farklı açı ve koşullarda alınan görseller ile Evrişimli Sinir Ağı tabanlı SSD MobileNet kütüphanesi kullanılarak nesne tespit etmeyi amaçlamaktadır. İHA üzerinden kamera ile elde edilen görüntüler NVIDIA Jetson Nano gömülü sistem bilgisayarı ile işlenmiş ve sınıflandırılmıştır. Gerçek zamanlı hedef tespitinde karşılaşılan problemlerin başında, değişken hava koşulları ve aydınlatma sebebiyle düşük çözünürlüklü elde edilen hareketli nesnelerin sınıflandırılması yer almaktadır. Bu duruma derin öğrenme kütüphanesinde yer alan öğrenilmiş görsellerle, kameradan alınan görseller arasındaki yakın özellikli veriler eşleştirilerek çözüm aranmaktadır. Bu çalışmada gömülü sistem içerisinde bir algoritma oluşturularak elde edilen görüntüler üzerinde düzenleme yapılmış, derin öğrenme kütüphanesi ile özellik karşılaştırma işleminin ardından elde edilen çıktılar nesne sınırı ve mAP (mean Average Precision) ortalama hassasiyet yüzdesi kullanılarak değerlendirilmiştir. Sonuç olarak, İHA üzerinden alınmış gerçek zamanlı görsel verilerden insan tespiti için %95.5 mAP son kesinlik ve %69.45 mAP ortalama hassasiyet, araç tespiti için %83.4 mAP son kesinlik ve % 64.5 mAP ortalama hassasiyet elde edilmiştir.

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Object Identification and Tracking Using YOLO Model: A CNN-Based Approach

Cited by 9 publications

References 6 publications

Image Enhancement using Convolution Neural Networks

Image Enhancement using Convolution Neural Networks

Robust Vehicle Detection Based on Improved You Look Only Once

İnsansız Hava Araçlarında Gömülü Sistem Üzerinden Derin Öğrenme ile Nesne Tespiti

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