Efficient object detection and classification on low power embedded systems

Jagannathan, Shyam; Desappan, Kumar; Swami, Pramod; Mathew, Manu; Nagori, Soyeb; Chitnis, Kedar; Marathe, Yogesh; Poddar, Deepak; Narayanan, Suriya N.

doi:10.1109/icce.2017.7889296

Cited by 14 publications

(13 citation statements)

References 4 publications

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“…Although additional modifications on the Fast R-CNN were made to fit TK1, the detection speed was very low (1.85 frames per second-fps). The work in [20] ran a seven-layer CNN on TDA3x SoC for object classification, and the overall system performance was 15 fps. Therefore, a powerful software/hardware platform is needed to support efficient embedded deep learning based real-time video processing.…”

Section: Embedded Object Recognitionmentioning

confidence: 99%

Embedded Deep Learning for Ship Detection and Recognition

2019

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Ship detection and recognition are important for smart monitoring of ships in order to manage port resources effectively. However, this is challenging due to complex ship profiles, ship background, object occlusion, variations of weather and light conditions, and other issues. It is also expensive to transmit monitoring video in a whole, especially if the port is not in a rural area. In this paper, we propose an on-site processing approach, which is called Embedded Ship Detection and Recognition using Deep Learning (ESDR-DL). In ESDR-DL, the video stream is processed using embedded devices, and we design a two-stage neural network named DCNet, which is composed of a DNet for ship detection and a CNet for ship recognition, running on embedded devices. We have extensively evaluated ESDR-DL, including performance of accuracy and efficiency. The ESDR-DL is deployed at the Dongying port of China, which has been running for over a year and demonstrates that it can work reliably for practical usage.

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Section: Embedded Object Recognitionmentioning

confidence: 99%

Embedded Deep Learning for Ship Detection and Recognition

2019

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“…Machine learning-based techniques are widely discussed, studied and applied for image classification, image recognition, and object detection in many fields [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The related application cases of machine learning-based image detection and classification are introduced as follows.…”

Section: Related Workmentioning

confidence: 99%

“…For traffic applications [3][4][5][6][7][8]19], Lousier and Abdelkrim [3] proposed a bag of features (Bove)-based machine learning framework for image classification, and this assessed the performance of training models using different image classification algorithms on the Caltech 101 images [4]. These authors also adopted the proposed BoF-based machine-learning framework to identify stop sign images for applying the trained classifier in a robotic system.…”

Section: Related Workmentioning

confidence: 99%

“…Jagannathan et al [6] implemented an embedded system-based object detection and classification mechanism that adopted a commercial SoC solution. The main components of this commercial SoC solution are fixed/floating-point dual DSP cores, a fully programmable VisionAccelerationPac (EVE), dual ARM-based Cortex M4 cores, and an image signal processor.…”

Section: Related Workmentioning

confidence: 99%

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Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

2018

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Featured Application: Deep learning, decision tree, linear discriminant analysis (LDA), support vector machines (SVMs), k-nearest neighbors algorithm (K-NN), and ensemble learning are evaluated for detecting hairy scalp problems. To the best of our knowledge, we are the first case study to apply modern machine learning to the diagnosis and analysis of hairy scalp issues.Abstract: Deep learning has become the most popular research subject in the fields of artificial intelligence (AI) and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA), support vector machines (SVM), k-nearest neighbors algorithm (K-NN), and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW), with machine-learning classifiers, and histogram of oriented gradients (HOG)/pyramid histogram of oriented gradients (PHOG) with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10 −4 , and this accuracy is far higher than those achieved by BOW with SVM (80.50%) and PHOG with SVM (53.0%).

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“…HOG features are shape descriptors that represent an object in terms of intensity gradients in specific directions. In [7], the authors have exploited HOG features citing their properties of invariance with respect to transformations like rotation, deformities as well as lighting conditions. In another work [8], Gabor wavelet has been utilized.…”

Section: Introductionmentioning

confidence: 99%

Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

Pillai,

2019

IJEAT

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Object recognition in video surveillance systems is the primary and most significant challenge task in the field of image processing. Video Surveillance systems provides us continuous monitoring of the objects for the enhancement of security and control. This paper presents novel approach recognizing the objects using Shi-Tomasi approach for detecting the corners of the object and then applies the Lucas-Kanade techniques to extract the features of the objects. The main objective of this paper is providing precise recognition of objects and estimation of their location from an unknown scene. Whenever the object is recognized from extracted frames of the input video the background subtraction will be applied. Then the classification of the objects into their respective categories can be achieved using support vector machine classifier by supervised learning. In case of multiple objects of different classes in a single frame, a vector containing the classes of all the detected in that frame is produced as output. The results of this work are drawn in the MATLAB tool by considering the input video dataset taken from various sources and extracting the frames from the input video for the detection then the efficiency of the proposed techniques will be measured.

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Efficient object detection and classification on low power embedded systems

Cited by 14 publications

References 4 publications

Embedded Deep Learning for Ship Detection and Recognition

Embedded Deep Learning for Ship Detection and Recognition

Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

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