Complete Visualisation, Network Modeling and Training, Web Based Tool, for the Yolo Deep Neural Network Model in the Darknet Framework

Carata, Serban; Mihaescu, Roxana; Barnoviciu, Eduard; Chindea, Mihai; Ghenescu, Marian; Ghenescu, Veta

doi:10.1109/iccp48234.2019.8959758

Cited by 7 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It uses logistic classification compared to SoftMax which was used in YOLOv2 (Kamble et al , 2020; Hassan et al , 2019). It also uses DarkNet, which is a pre-trained model (Carata et al , 2019).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Object detection and activity recognition in video surveillance using neural networks

Payghode

Goyal

Bhan

et al. 2023

IJWIS

View full text Add to dashboard Cite

Purpose This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy. Design/methodology/approach The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods. Findings The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus. Originality/value This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

show abstract

Section: Methodsmentioning

confidence: 99%

“…DarkNet (Carata et al , 2019) is the open-source neural network framework written in C language and CUDA used in this research. CUDA helps in GPU computations to train the model faster.…”

Section: Methodsmentioning

confidence: 99%

Object detection and activity recognition in video surveillance using neural networks

Payghode

Goyal

Bhan

et al. 2023

IJWIS

View full text Add to dashboard Cite

show abstract

“…The model uses multiscale training (learning discriminative features at different spatial scales and locations) [44], data augmentation [45], and batch normalization techniques [46]. The framework used for training and testing was Darknet neural network [22].…”

Section: Model Overviewmentioning

confidence: 99%

An integral computer vision system for apple detection, classification, and semantic segmentation

Ashraf

Abbas

Haseeb

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

View full text Add to dashboard Cite

The area of Computer Vision has gone through exponential growth and advancement over the past decade. It is mainly due to the introduction of effective deep-learning methodologies and the availability of massive data. This has resulted in the incorporation of intelligent computer vision schemes to automate the different number of tasks. In this paper, we have worked on similar lines. We have proposed an integrated system for the development of robotic arms, considering the current situation in fruit identification, classification, counting, and generating their masks through semantic segmentation. The current method of manually doing these processes is time-consuming and is not feasible for large fields. Due to this, multiple works have been proposed to automate harvesting tasks to minimize the overall overhead. However, there is a lack of an integrated system that can automate all these processes together. As a result, we are proposing one such approach based on different machine learning techniques. For each process, we propose to use the most effective learning technique with computer vision capability. Thus, proposing an integrated intelligent end-to-end computer vision-based system to detect, classify, count, and identify the apples. In this system, we modified the YOLOv3 algorithm to detect and count the apples effectively. The proposed scheme works even under variable lighting conditions. The system was trained and tested using a standard benchmark i.e., MinneApple. Experimental results show an average accuracy of 91%.

show abstract

“…The PC specifications comprised a CPU (I7-10700F, Intel, Santa Clara, CA, USA), graphics processing unit (GPU) display card (RTX 3080 10G, AsusTeK, Taipei, Taiwan) and 64-gigabyte dynamic random-access memory. The neural network training framework Darknet [21] was used for training the AI model. The trained neural network is input to the conversion program and converted into a TensorFlow-based model that the KPU can infer.…”

Section: A System Overviewmentioning

confidence: 99%

Fall Detection System With Artificial Intelligence-Based Edge Computing

Lin

Peng

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

Complete Visualisation, Network Modeling and Training, Web Based Tool, for the Yolo Deep Neural Network Model in the Darknet Framework

Cited by 7 publications

References 6 publications

Object detection and activity recognition in video surveillance using neural networks

Object detection and activity recognition in video surveillance using neural networks

An integral computer vision system for apple detection, classification, and semantic segmentation

Fall Detection System With Artificial Intelligence-Based Edge Computing

Contact Info

Product

Resources

About