Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms

For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localization, object dimensions, and object orientation. Our method has been evaluated on KITTI’s road dataset as well as on our own hybrid virtual road/rail dataset acquired from the video game Grand Theft Auto (GTA) V. The evaluation of our method on these two datasets shows good accuracy, but more importantly that it can be used in real-time conditions, in road and rail traffic environments. Through our experimental results, we also show the importance of the accuracy of prediction of the regions of interest (RoIs) used in the estimation of 3D bounding box parameters.

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Real-Time Object Detection and Localization for Vision-Based Robot Manipulator

SN Computer Science ◽

10.1007/s42979-021-00561-4 ◽

2021 ◽

Vol 2 (3) ◽

Author(s):

Varun Batra ◽

Vijay Kumar

Keyword(s):

Object Detection ◽

Real Time ◽

Robot Manipulator ◽

Detection And Localization

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Real-Time Deep Learning-Based Object Detection Framework

2020 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci47803.2020.9308493 ◽

2020 ◽

Author(s):

William Tarimo ◽

Moustafa M.Sabra ◽

Shonan Hendre

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time

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Experimental Deep Learning Object Detection in Real-time Colonoscopies

10.1109/ehb52898.2021.9657740 ◽

2021 ◽

Author(s):

Adrian Ciobanu ◽

Mihaela Luca ◽

Tudor Barbu ◽

Vasile Drug ◽

Andrei Olteanu ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Learning Object

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Prediction of Crime Scene Objects Using Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37512 ◽

2021 ◽

Vol 9 (VIII) ◽

pp. 857-864

Author(s):

Vibhavari B Rao

Keyword(s):

Deep Learning ◽

Object Detection ◽

Computer Architecture ◽

Real Time ◽

Surveillance System ◽

Crime Scene ◽

Detection Model ◽

Detection Algorithms ◽

Performance Metric ◽

Smart Surveillance

The crime rates today can inevitably put a civilian's life in danger. While consistent efforts are being made to alleviate crime, there is also a dire need to create a smart and proactive surveillance system. Our project implements a smart surveillance system that would alert the authorities in real-time when a crime is being committed. During armed robberies and hostage situations, most often, the police cannot reach the place on time to prevent it from happening, owing to the lag in communication between the informants of the crime scene and the police. We propose an object detection model that implements deep learning algorithms to detect objects of violence such as pistols, knives, rifles from video surveillance footage, and in turn send real-time alerts to the authorities. There are a number of object detection algorithms being developed, each being evaluated under the performance metric mAP. On implementing Faster R-CNN with ResNet 101 architecture we found the mAP score to be about 91%. However, the downside to this is the excessive training and inferencing time it incurs. On the other hand, YOLOv5 architecture resulted in a model that performed very well in terms of speed. Its training speed was found to be 0.012 s / image during training but naturally, the accuracy was not as high as Faster R-CNN. With good computer architecture, it can run at about 40 fps. Thus, there is a tradeoff between speed and accuracy and it's important to strike a balance. We use transfer learning to improve accuracy by training the model on our custom dataset. This project can be deployed on any generic CCTV camera by setting up a live RTSP (real-time streaming protocol) and streaming the footage on a laptop or desktop where the deep learning model is being run.

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