Moving Object Detection in Real-Time Using Stereo from a Mobile Platform

This paper presents a mobile object detection algorithm which performs with two consecutive stereo images. Like most motion detection methods, the proposed one is based on dense stereo matching and optical flow (OF) estimation. Noting that the main computational cost of existing methods is related to the estimation of OF, we propose to use a fast algorithm based on Lucas–Kanade paradigm. We then derive a comprehensive uncertainty model by taking into account all the estimation errors occurring during the process. In contrast with most previous works, we rigorously expand the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the challenging KITTI dataset which demonstrates the effectiveness of the proposed approach.

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Revisiting knowledge distillation for light-weight visual object detection

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211022877 ◽

2021 ◽

Vol 43 (13) ◽

pp. 2888-2898

Author(s):

Tianze Gao ◽

Yunfeng Gao ◽

Yu Li ◽

Peiyuan Qin

Keyword(s):

Object Detection ◽

Essential Element ◽

Detection Algorithm ◽

Positive Sample ◽

Detection Methods ◽

Visual Object ◽

Light Weight ◽

Model Compression ◽

Novel Approach ◽

Knowledge Distillation

An essential element for intelligent perception in mechatronic and robotic systems (M&RS) is the visual object detection algorithm. With the ever-increasing advance of artificial neural networks (ANN), researchers have proposed numerous ANN-based visual object detection methods that have proven to be effective. However, networks with cumbersome structures do not befit the real-time scenarios in M&RS, necessitating the techniques of model compression. In the paper, a novel approach to training light-weight visual object detection networks is developed by revisiting knowledge distillation. Traditional knowledge distillation methods are oriented towards image classification is not compatible with object detection. Therefore, a variant of knowledge distillation is developed and adapted to a state-of-the-art keypoint-based visual detection method. Two strategies named as positive sample retaining and early distribution softening are employed to yield a natural adaption. The mutual consistency between teacher model and student model is further promoted through a hint-based distillation. By extensive controlled experiments, the proposed method is testified to be effective in enhancing the light-weight network’s performance by a large margin.

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Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

Remote Sensing ◽

10.3390/rs13224610 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4610

Author(s):

Li Zhu ◽

Zihao Xie ◽

Jing Luo ◽

Yuhang Qi ◽

Liman Liu ◽

...

Keyword(s):

Object Detection ◽

Feature Fusion ◽

Computational Cost ◽

Detection Algorithm ◽

Dynamic Object ◽

Shared Feature ◽

Detection Model ◽

Detection Algorithms ◽

Adaptive Inference ◽

Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

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Lightweight Detection Network Based on Sub-Pixel Convolution and Objectness-Aware Structure for UAV Images

Sensors ◽

10.3390/s21165656 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5656

Author(s):

Xuanye Li ◽

Hongguang Li ◽

Yalong Jiang ◽

Meng Wang

Keyword(s):

Object Detection ◽

Computational Cost ◽

Frame Rate ◽

Detection Methods ◽

Detection Accuracy ◽

Small Object ◽

Mobile Platforms ◽

Efficient Detection ◽

Uav Images ◽

Uav Image

Unmanned Aerial Vehicles (UAVs) can serve as an ideal mobile platform in various situations. Real-time object detection with on-board apparatus provides drones with increased flexibility as well as a higher intelligence level. In order to achieve good detection results in UAV images with complex ground scenes, small object size and high object density, most of the previous work introduced models with higher computational burdens, making deployment on mobile platforms more difficult.This paper puts forward a lightweight object detection framework. Besides being anchor-free, the framework is based on a lightweight backbone and a simultaneous up-sampling and detection module to form a more efficient detection architecture. Meanwhile, we add an objectness branch to assist the multi-class center point prediction, which notably improves the detection accuracy and only takes up very little computing resources. The results of the experiment indicate that the computational cost of this paper is 92.78% lower than the CenterNet with ResNet18 backbone, and the mAP is 2.8 points higher on the Visdrone-2018-VID dataset. A frame rate of about 220 FPS is achieved. Additionally, we perform ablation experiments to check on the validity of each part, and the method we propose is compared with other representative lightweight object detection methods on UAV image datasets.

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Mathematical Morphology Based Moving Object Detection Algorithm under Complicated Background

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.361 ◽

2014 ◽

Vol 596 ◽

pp. 361-364

Author(s):

Jun Zhou

Keyword(s):

Object Detection ◽

Mathematical Morphology ◽

Detection Algorithm ◽

Moving Object Detection ◽

Moving Object ◽

Detection Methods ◽

Modern World ◽

Important Work ◽

Opening And Closing ◽

Conventional Detection

In many applications of modern world, moving object detection is an important work. Especially, the detection of moving object under complicated background has more difficult. On the basis of introduction of conventional detection methods such as temporal difference detection (TDD) and background subtraction detection, the paper proposes a new mathematical morphology based moving object detection algorithm in view of complicated background. The proposed algorithm utilizes the opening and closing operations to process the temporal differenced image. Then, it compares the processed image with the background differenced image. The experimental results show that the proposed algorithm can be able to detect the moving object effectively under complicatedly external environment and has a higher detection precision in contrast with the conventional temporal detection method..

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Object Detection Algorithm Based on Improved YOLOv3

Electronics ◽

10.3390/electronics9030537 ◽

2020 ◽

Vol 9 (3) ◽

pp. 537 ◽

Cited By ~ 5

Author(s):

Liquan Zhao ◽

Shuaiyang Li

Keyword(s):

Markov Chains ◽

Object Detection ◽

Large Scale ◽

Ground Truth ◽

Detection Algorithm ◽

Detection Methods ◽

Cluster Method ◽

Cluster Center ◽

Initial Cluster ◽

Bounding Boxes

The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width and height are sensitive to the initial cluster centers, and the processing of large-scale datasets is time-consuming. In order to address these problems, a new cluster method for estimating the initial width and height of the predicted bounding boxes has been developed. Firstly, it randomly selects a couple of width and height values as one initial cluster center separate from the width and height of the ground truth boxes. Secondly, it constructs Markov chains based on the selected initial cluster and uses the final points of every Markov chain as the other initial centers. In the construction of Markov chains, the intersection-over-union method is used to compute the distance between the selected initial clusters and each candidate point, instead of the square root method. Finally, this method can be used to continually update the cluster center with each new set of width and height values, which are only a part of the data selected from the datasets. Our simulation results show that the new method has faster convergence speed for initializing the width and height of the predicted bounding boxes and that it can select more representative initial widths and heights of the predicted bounding boxes. Our proposed method achieves better performance than the YOLOv3 method in terms of recall, mean average precision, and F1-score.

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Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

Mathematical Problems in Engineering ◽

10.1155/2020/6641491 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Manhuai Lu ◽

Liqin Chen

Keyword(s):

Object Detection ◽

Detection Method ◽

Specular Reflection ◽

Detection Algorithm ◽

Detection Methods ◽

External Disturbances ◽

Feature Enhancement ◽

Art Object ◽

Testing Dataset ◽

Pattern Information

The accuracy of object detection based on kitchen appliance scene images can suffer severely from external disturbances such as various levels of specular reflection, uneven lighting, and spurious lighting, as well as internal scene-related disturbances such as invalid edges and pattern information unrelated to the object of interest. The present study addresses these unique challenges by proposing an object detection method based on improved faster R-CNN algorithm. The improved method can identify object regions scattered in various areas of complex appliance scenes quickly and automatically. In this paper, we put forward a feature enhancement framework, named deeper region proposal network (D-RPN). In D-RPN, a feature enhancement module is designed to more effectively extract feature information of an object on kitchen appliance scene. Then, we reconstruct a U-shaped network structure using a series of feature enhancement modules. We have evaluated the proposed D-RPN on the dataset we created. It includes all kinds of kitchen appliance control panels captured in nature scene by image collector. In our experiments, the best-performing object detection method obtained a mean average precision mAP value of 89.84% in the testing dataset. The test results show that the proposed improved algorithm achieves higher detecting accuracy than state-of-the-art object detection methods. Finally, our proposed detection method can further be used in text recognition.

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SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6958 ◽

2020 ◽

Vol 34 (07) ◽

pp. 12661-12668 ◽

Cited By ~ 3

Author(s):

Lewei Yao ◽

Hang Xu ◽

Wei Zhang ◽

Xiaodan Liang ◽

Zhenguo Li

Keyword(s):

Object Detection ◽

Feature Fusion ◽

State Of The Art ◽

Computational Cost ◽

Search Space ◽

Detection Methods ◽

Structural Level ◽

Neural Architecture ◽

Art Object ◽

Searching Strategy

The state-of-the-art object detection method is complicated with various modules such as backbone, RPN, feature fusion neck and RCNN head, where each module may have different designs and structures. How to leverage the computational cost and accuracy trade-off for the structural combination as well as the modular selection of multiple modules? Neural architecture search (NAS) has shown great potential in finding an optimal solution. Existing NAS works for object detection only focus on searching better design of a single module such as backbone or feature fusion neck, while neglecting the balance of the whole system. In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection. Specifically, Structural-level searching stage first aims to find an efficient combination of different modules; Modular-level searching stage then evolves each specific module and pushes the Pareto front forward to a faster task-specific network. We consider a multi-objective search where the search space covers many popular designs of detection methods. We directly search a detection backbone without pre-trained models or any proxy task by exploring a fast training from scratch strategy. The resulting architectures dominate state-of-the-art object detection systems in both inference time and accuracy and demonstrate the effectiveness on multiple detection datasets, e.g. halving the inference time with additional 1% mAP improvement compared to FPN and reaching 46% mAP with the similar inference time of MaskRCNN.

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Saliency detection in deep learning era: trends of development

Information and Control Systems ◽

10.31799/1684-8853-2019-3-10-36 ◽

2019 ◽

pp. 10-36 ◽

Cited By ~ 2

Author(s):

M. N. Favorskaya ◽

L. C. Jain

Keyword(s):

Deep Learning ◽

Object Detection ◽

Event Detection ◽

Visual Analysis ◽

Saliency Detection ◽

Salient Object Detection ◽

Public Image ◽

Detection Methods ◽

Salient Object ◽

Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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