Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provide a comprehensive survey of the recent CNNs based crowd counting techniques. We briefly review detection-based, regression-based, and traditional density estimation based approaches. Then, we delve into detail regarding the deep learning based density estimation approaches and recently published datasets. In addition, we discuss the potential applications of crowd counting and in particular its applications using unmanned aerial vehicle (UAV) images.

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Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

Remote Sensing ◽

10.3390/rs13132627 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2627

Author(s):

Marks Melo Moura ◽

Luiz Eduardo Soares de Oliveira ◽

Carlos Roberto Sanquetta ◽

Alexis Bastos ◽

Midhun Mohan ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Forest Restoration ◽

Amazon Forest ◽

Forest Species ◽

Accuracy And Precision ◽

Identification Of Species ◽

Aerial Vehicle ◽

High Resolution Images ◽

Uav Images

Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.

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Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

PLoS ONE ◽

10.1371/journal.pone.0255586 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255586

Author(s):

Chiaki Yamato ◽

Kotaro Ichikawa ◽

Nobuaki Arai ◽

Kotaro Tanaka ◽

Takahiro Nishiyama ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Binary Classification ◽

Aerial Photographs ◽

Seagrass Habitat ◽

Aerial Vehicle ◽

The Mean ◽

Intertidal Seagrass ◽

Uav Images ◽

Manual Correction

Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs’ feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs’ feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model’s accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12–27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs’ seagrass habitat use.

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Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs12121994 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1994 ◽

Cited By ~ 1

Author(s):

Xin Luo ◽

Xiaoyue Tian ◽

Huijie Zhang ◽

Weimin Hou ◽

Geng Leng ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Vehicle Detection ◽

Neural Computing ◽

Detection Capability ◽

High Processing Speed ◽

Aerial Vehicle ◽

Uav Images ◽

Uav Image ◽

High Processing

Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.

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Road Traffic Monitoring from UAV Images Using Deep Learning Networks

Remote Sensing ◽

10.3390/rs13204027 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4027

Author(s):

Sungwoo Byun ◽

In-Kyoung Shin ◽

Jucheol Moon ◽

Jiyoung Kang ◽

Sang-Il Choi

Keyword(s):

Mean Squared Error ◽

Road Traffic ◽

Recall Rate ◽

Traffic Monitoring ◽

Learning Networks ◽

Additional Information ◽

Squared Error ◽

Aerial Vehicle ◽

Uav Images ◽

Precision Rate

In this paper, we propose a deep neural network-based method for estimating speed of vehicles on roads automatically from videos recorded using unmanned aerial vehicle (UAV). The proposed method includes the following; (1) detecting and tracking vehicles by analyzing the videos, (2) calculating the image scales using the distances between lanes on the roads, and (3) estimating the speeds of vehicles on the roads. Our method can automatically measure the speed of the vehicles from the only videos recorded using UAV without additional information in both directions on the roads simultaneously. In our experiments, we evaluate the performance of the proposed method with the visual data at four different locations. The proposed method shows 97.6% recall rate and 94.7% precision rate in detecting vehicles, and it shows error (root mean squared error) of 5.27 km/h in estimating the speeds of vehicles.

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The machine training in problems of satellite images’s processing

Metrologiya ◽

10.32446/0132-4713.2020-4-15-37 ◽

2020 ◽

pp. 15-37

Author(s):

L. P. Bass ◽

Yu. A. Plastinin ◽

I. Yu. Skryabysheva

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Computer Vision ◽

Satellite Images ◽

Vision Systems ◽

Earth Remote Sensing ◽

Practical Applications ◽

Convolution Neural Networks ◽

Computer Vision Systems ◽

Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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