A computer vision-based object detection and counting for COVID-19 protocol compliance: a case study of Jakarta

2021 ◽

pp. 501-508

Author(s):

Gopal Sakarkar ◽

Rashmi Baitule

Keyword(s):

Computer Vision ◽

Object Detection ◽

Fruits And Vegetables ◽

Machine Intelligence ◽

Detection Techniques ◽

Agricultural Applications ◽

Rgb Images ◽

Computer Vision Applications

Automated or robot-assisted collection is an evolving research domain that mixes aspects of machine vision and machine intelligence. When combined with robotics, image processing has proven to be an efficient method for analysis in various performance areas, namely agricultural applications. Most of it had been applied to the robot, which may want to pick fruit and type various fruits and vegetables. Identification and classification could even be a serious obstacle to computer vision demanding near-human levels of recognition. The target of this survey is to classify and briefly review the literature on harvesting robots that use different techniques and computer analysis of images of fruits and vegetables in agricultural activities, which incorporates 25 articles published within the last three decades. The proposed approach takes under consideration various sorts of fruit. Much research on this subject has been conducted in recent years, either implementing simple techniques such as computer vision like color-based clustering or using other sensors like LWIR, hyperspectral, or 3D. Current advances in computer vision offer an honest sort of advanced object detection techniques that would dramatically increase the quality of efficiency of fruit detection from RGB images. Some performance evaluation metrics obtained in various experiments are emphasized for the revised techniques, thus helping researchers to settle on and make new computer vision applications in fruit images.

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Investigation on Error-Tolerability Enhancement of Videos via Re-Encoding for Computer Vision: A Case Study on Object Detection

10.1109/icce-tw52618.2021.9602931 ◽

2021 ◽

Author(s):

Tong-Yu Hsieh ◽

Jun-Tsung Wu

Keyword(s):

Computer Vision ◽

Object Detection

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ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

Computer systems and network ◽

10.23939/csn2020.01.080 ◽

2017 ◽

Vol 2 (1) ◽

pp. 80-87

Author(s):

Puyda V. ◽

◽

Stoian. A.

Keyword(s):

Computer Vision ◽

Object Detection ◽

Open Source ◽

Feature Detection ◽

Video Stream ◽

Object Identification ◽

Vision Systems ◽

Modern Computer ◽

Computer Vision Systems ◽

Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

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Measurement of Gem Colour Using a Computer Vision System: A Case Study with Jadeite-Jade

Minerals ◽

10.3390/min11080791 ◽

2021 ◽

Vol 11 (8) ◽

pp. 791

Author(s):

Sufei Zhang ◽

Ying Guo

Keyword(s):

Computer Vision ◽

Regression Model ◽

Vision System ◽

Expert Group ◽

Group Test ◽

System A ◽

Real Objects ◽

Practical Tool ◽

Almost All

This paper introduces computer vision systems (CVSs), which provides a new method to measure gem colour, and compares CVS and colourimeter (CM) measurements of jadeite-jade colour in the CIELAB space. The feasibility of using CVS for jadeite-jade colour measurement was verified by an expert group test and a reasonable regression model in an experiment involving 111 samples covering almost all jadeite-jade colours. In the expert group test, more than 93.33% of CVS images are considered to have high similarities with real objects. Comparing L*, a*, b*, C*, h, and ∆E* (greater than 10) from CVS and CM tests indicate that significant visual differences exist between the measured colours. For a*, b*, and h, the R2 of the regression model for CVS and CM was 90.2% or more. CVS readings can be used to predict the colour value measured by CM, which means that CVS technology can become a practical tool to detect the colour of jadeite-jade.

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Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

Journal of Imaging ◽

10.3390/jimaging7040064 ◽

2021 ◽

Vol 7 (4) ◽

pp. 64

Author(s):

Tanguy Ophoff ◽

Cédric Gullentops ◽

Kristof Van Beeck ◽

Toon Goedemé

Keyword(s):

Computational Complexity ◽

Object Detection ◽

Network Optimization ◽

Real Life ◽

Optimization Techniques ◽

Training Data ◽

Single Shot ◽

Standard Object ◽

Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

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Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

Remote Sensing ◽

10.3390/rs13010023 ◽

2020 ◽

Vol 13 (1) ◽

pp. 23

Author(s):

Wei Zhao ◽

William Yamada ◽

Tianxin Li ◽

Matthew Digman ◽

Troy Runge

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Precision Agriculture ◽

Crop Production ◽

Domain Adaptation ◽

Training Data ◽

Detection Accuracy ◽

Detection Model ◽

Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

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