A real-time object detection algorithm for video

Lu, Shengyu; Wang, Beizhan; Wang, Hongji; Chen, Lihao; Ma, Linjian; Zhang, Xiaoyan

doi:10.1016/j.compeleceng.2019.05.009

Cited by 87 publications

(38 citation statements)

References 18 publications

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“…Since being proposed by Hinton in 2006 [19], deep learning theory has resulted in significant progress in scene recognition, object detection, and remote sensing image classification [20][21][22][23][24][25]. The most representative architecture is the convolutional neural network (CNN), which is a multilayer neural network whose design is derived from the concept of subregions and the hierarchical analysis revealed by study of the mammalian visual cortex [26].…”

Section: Introductionmentioning

confidence: 99%

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

2020

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Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China.

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Section: Introductionmentioning

confidence: 99%

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

2020

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“…In [33], the authors proposed an innovative method to determine the performances of object recognition algorithms in the videos, highlighting specific features of the particular method, such as region splitting or merging; the method relies on the comparison between the output of the recognition algorithm and correct split segment extracted with 1 frame/s sampling rate. Similarly, Lu et al, in [38], introduced a real-time object detection framework for video, employing the You Only Look Once (YOLO) network, with an improved convolution method for speeding up the elaboration, and thus object detection. Through a preprocessing, the effects of the background are removed, as well as the processing noise.…”

Section: Overview Of Applications and Innovative Methods For Visual Rmentioning

confidence: 99%

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

et al. 2020

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This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

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“…Accordingly, S. Lu et al [24] proposed a real-time object detection algorithm in the video. The proposed algorithm trained the Fast-YOLO model to preprocess the image to remove the background and obtain object information.…”

Section: Image Object Detection Algorithmmentioning

confidence: 99%

“…Since YOLO learns the general characteristics of objects, it can be predicted even when new data is entered [23]. Accordingly, S. Lu et al [24] proposed a real-time object detection algorithm in the video. The proposed algorithm trained the Fast-YOLO model to preprocess the image to remove the background and obtain object information.…”

Section: Appl Sci 2020 10 X For Peer Review 3 Of 19mentioning

confidence: 99%

Pothole Classification Model Using Edge Detection in Road Image

Baek

Chung

2020

Applied Sciences

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Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

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A real-time object detection algorithm for video

Cited by 87 publications

References 18 publications

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

Pothole Classification Model Using Edge Detection in Road Image

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