In the recent times, there has been a lot of speculation related to advanced driver-assistance system (ADAS) which provides best driving experience for the drivers. ADAS technology helps to detect the unhealthy driving conditions which lead to road accidents today. Road accidents are basically caused due to distracted driving, over speeding, drink and drive, foggy weather, no proper headlights, or due to some object which suddenly trespasses the vehicle. Today the major advancements in ADAS include parking assistance, road traffic detection, object detection on highways, and lane detection. But the major risk limitation in ADAS system is the speed and time at which the object is detected and tracked. Several algorithms such as R-CNN, Fast R-CNN, and YOLO were used for effective object detection and tracking earlier, but sometimes, the system do fail to detect due the speed factor. Hence, the proposed work presents a novel approach called “A Real-Time Object Detection Framework for Advanced Driver Assistant Systems” by implementing the state-of-the-art object detection algorithm YOLOv5 which improves the speed in detection of object over real-time. This paper provides a comparison between other state-of-the-art object detectors such as YOLOv3 and YOLOv4. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three benchmark datasets collected as a part of research findings. YOLOv5 proves to be faster and 95% accurate than the other object detection algorithms in the comparison. This framework is used to build a mobile application called “ObjectDetect” which helps users make better decisions on the road. “ObjectDetect” consists of a simple user interface that displays alerts and warnings.

/pdf/objectdetect-a-real-time-object-detection-framework-for-1imfno2o.pdf

ObjectDetect: A Real-Time Object Detection Framework for Advanced Driver Assistant Systems Using YOLOv5

A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

/pdf/the-object-detection-perspective-and-obstacles-in-robotic-a-3txofprc.pdf

The Object Detection, Perspective and Obstacles In Robotic: A Review

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012068/pdf

Real-time vehicle detection and tracking in video based on faster R-CNN

In latest years, deep neural networks were observed to be the most influential among all innovations in the computer vision field, generating remarkable performance on image classification. Convolution neural networks (CNNs) are considering as an interesting tool for studying vision of biological because this category of artificial vision systems shows the capabilities of visual recognition similar to those of human observers. By improving the recognition performance of these models, it appears that they become more effective in prediction. Recent benchmarks have shown that deep CNNs are excellent approaches for object recognition and detection. In this paper, we are focusing on the core building blocks of convolution neural networks architecture. Different object detection methods that utilize convolution neural networks are discussed and compared. On the other hand, there is a simple summary of the common CNNs architectures.

https://iopscience.iop.org/article/10.1088/1742-6596/1804/1/012095/pdf

Survey: Convolution Neural networks in Object Detection

Object detection in remote sensing imagery is an important topic of image processing researches. Detection of objects and regions from satellite imagery takes its place in various applications such as the detection of residential areas and agricultural lands, road lines, ships, as well as airport and hangar detections. As a more specific remote sensing imagery based object-detection process, a stationary aircraft detection system could serve as a model in some military applications. Such a model could serve the detection of stationary aircraft targets in airports. In the proposed study, a deep learning-based model detects the aircraft in the airports using the satellite images from Google Earth. The deep learning model uses the state of the art Regions with Convolutional Neural Network (RCNN). Firstly a built from scratch CNN design is used for the basic learning step of the system. Then, RCNN performs region detection, which anchors the stationary aircraft object bounding boxes. A large dataset containing aircraft images is preferred for the training of CNN. To validate the system, satellite images captured from airports in Turkey are used. The results of the study show that the proposed model successfully operates aircraft detection with high-performance rates. While the classifier network structure, which constitutes the first step of the study, produces 98.4% test accuracy, the proposed aircraft detection framework has successfully performed the aircraft identification process by producing matched bounding boxes in the test images.

https://www.ijisae.org/IJISAE/article/download/1094/629

Aircraft Detection System Based on Regions with Convolutional Neural Networks

Object detection is one of the important problems for autonomous robots. Faster R-CNN, one of the state-of-the-art object detection methods, approaches real time application; nevertheless, computational time lies borderline of real time application, i.e. 5fps with VGG16 model in K40 GPU system in [1]. Moreover, computation time depends on model and image crop size, but precision is also affected; usually, time and precision have trade-off relation. By adjusting input image size in spite of downgrading performance, computation time meets criteria for one model. Therefore, selection of a model is one of the important problems when faster R-CNN based object detection system for an autonomous robot is constructed. In this paper, we convert several state-of-the-art models from convolution neural network (CNN) for image classification. Then, we compare converted models with several image crop size in terms of computation time and detection precision. We will utilize those comparison data for selecting a proper detection model in case a robot needs to perform an object detection task.

Comparison of faster R-CNN models for object detection

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Comparison of faster R-CNN models for object detection