Deep Learning Algorithms-based Object Detection and Localization Revisited

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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Trajectory Object Detection using Deep Learning Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6564.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7895-7898

Keyword(s):

Deep Learning ◽

Object Detection ◽

Large Scale ◽

Smart Cities ◽

Learning Algorithms ◽

Big Data Analytics ◽

Light Condition ◽

Transport Systems ◽

Video Footage ◽

Vehicle Type

Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection.

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Medicine Delivery Bot Using Time Series and Object Detection

International Journal of Scientific Research in Science and Technology ◽

10.32628/cseit217469 ◽

2021 ◽

pp. 286-290

Author(s):

Kartikeya Bajpai ◽

Prachi Jain

Keyword(s):

Time Series ◽

Deep Learning ◽

Object Detection ◽

Learning Algorithms ◽

Research Paper ◽

Traffic Light ◽

Lapse Time ◽

Manual Work ◽

Traffic Lights ◽

Present Context

Nowadays, delivery is mainly done by humans which includes a lot of manual work. The existing way is good but lacks faster deliveries. In the present context the deliveries are not possible 24*7 by humans, especially in the case of medicines, customers often require immediate deliveries for maintaining their course of medication. Since, in many other fields AI has contributed to decreasing a lot of manual work and time. In this research paper, we have proposed the idea of a delivery bot which uses deep learning algorithms to detect traffic lights and classify the color of the traffic light. On the basis of which the lapse time will be calculated in between the two traffic lights and hence maps the route for delivery with the help of geocoding accordingly which helps in more secure and faster deliveries.

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DiaMole: Mole Detection and Segmentation Software for Mobile Phone Skin Images

Journal of Healthcare Engineering ◽

10.1155/2021/6698176 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Shuai Liu ◽

Zheng Chen ◽

Huahui Zhou ◽

Kunlin He ◽

Meiyu Duan ◽

...

Keyword(s):

Deep Learning ◽

Object Detection ◽

Mobile Phone ◽

Early Stage ◽

Learning Algorithms ◽

Skin Lesions ◽

Detection Algorithm ◽

Incidence And Mortality ◽

Malignant Skin ◽

User Friendly

Motivation. The worldwide incidence and mortality rates of melanoma are on the rise recently. Melanoma may develop from benign lesions like skin moles. Easy-to-use mole detection software will help find the malignant skin lesions at the early stage. Results. This study developed mole detection and segmentation software DiaMole using mobile phone images. DiaMole utilized multiple deep learning algorithms for the object detection problem and mole segmentation problem. An object detection algorithm generated a rectangle tightly surrounding a mole in the mobile phone image. Moreover, the segmentation algorithm detected the precise boundary of that mole. Three deep learning algorithms were evaluated for their object detection performance. The popular performance metric mean average precision (mAP) was used to evaluate the algorithms. Among the utilized algorithms, the Faster R-CNN could achieve the best mAP = 0.835, and the integrated algorithm could achieve the mAP = 0.4228. Although the integrated algorithm could not achieve the best mAP, it can avoid the missing of detecting the moles. A popular Unet model was utilized to find the precise mole boundary. Clinical users may annotate the detected moles based on their experiences. Conclusions. DiaMole is user-friendly software for researchers focusing on skin lesions. DiaMole may automatically detect and segment the moles from the mobile phone skin images. The users may also annotate each candidate mole according to their own experiences. The automatically calculated mole image masks and the annotations may be saved for further investigations.

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

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/261/1/012005 ◽

2017 ◽

Vol 261 ◽

pp. 012005 ◽

Cited By ~ 2

Author(s):

F. Particke ◽

R. Kolbenschlag ◽

M. Hiller ◽

L. Patiño-Studencki ◽

J. Thielecke

Keyword(s):

Deep Learning ◽

Object Detection ◽

Real Time ◽

Mobile Platforms ◽

Detection And Localization

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Understanding Deep Learning Algorithms for Object Detection and Recognition

2019 11th International Conference on Advanced Computing (ICoAC) ◽

10.1109/icoac48765.2019.247137 ◽

2019 ◽

Author(s):

S Suriya ◽

Rajesh Harinarayanan Rajasekar ◽

S. Mercy Shalinie

Keyword(s):

Deep Learning ◽

Object Detection ◽

Learning Algorithms ◽

Detection And Recognition

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Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms

Construction Innovation ◽

10.1108/ci-02-2020-0017 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Chang Liu ◽

Samad M.E. Sepasgozar ◽

Sara Shirowzhan ◽

Gelareh Mohammadi

Keyword(s):

Deep Learning ◽

Object Detection ◽

Learning Algorithms ◽

Single Shot ◽

Modular Construction ◽

Construction Sites ◽

Validation Data ◽

Data Set ◽

Content Type ◽

Detection Algorithms

Purpose The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection. Design/methodology/approach This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences. Findings The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects. Research limitations/implications The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner. Originality/value This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

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