scholarly journals Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Etienne David ◽  
Mario Serouart ◽  
Daniel Smith ◽  
Simon Madec ◽  
Kaaviya Velumani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Agricultural Science ◽  
Detection Methods ◽  
Head Detection ◽  
Rgb Images

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

scholarly journals Animal Detection in Highly Cluttered Natural Scenes by using Faster R-CNN

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1311-1313
Keyword(s):  
Computer Vision ◽  
Environmental Protection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Natural Scenes ◽  
Human Beings ◽  
Intelligent Monitoring ◽  
Animal Detection ◽  
Fast Region ◽  
Important Branch

With the increasing awareness of environmental protection, people are paying more and more attention to the protection of wild animals. Their survive-al is closely related to human beings. As progress in target detection has achieved unprecedented success in computer vision, we can more easily tar-get animals. Animal detection based on computer vision is an important branch of object recognition, which is applied to intelligent monitoring, smart driving, and environmental protection. At present, many animal detection methods have been proposed. However, animal detection is still a challenge due to the complexity of the background, the diversity of animal pos-es, and the obstruction of objects. An accurate algorithm is needed. In this paper, the fast Region-based Convolutional Neural Network (Faster R-CNN) is used. The proposed method was tested using the CAMERA_TRAP DATASET. The results show that the proposed animal detection method based on Faster R-CNN performs better in terms of detection accuracy when its performance is compared to conventional schemes

scholarly journals Extraction and Research of Crop Feature Points Based on Computer Vision

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2553 ◽  
Author(s):  
Jingwen Cui ◽  
Jianping Zhang ◽  
Guiling Sun ◽  
Bowen Zheng
Keyword(s):  
Computer Vision ◽  
Point Cloud ◽  
High Accuracy ◽  
Depth Camera ◽  
Positioning Error ◽  
Feature Points ◽  
Matching Method ◽  
Depth Images ◽  
Computer Vision Technology ◽  
Rgb Images

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

scholarly journals TCM: Temporal Consistency Model for Head Detection in Complex Videos

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sultan Daud Khan ◽  
Ahmed B. Altamimi ◽  
Mohib Ullah ◽  
Habib Ullah ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Real World ◽  
Experimental Evaluation ◽  
Research Problem ◽  
Temporal Consistency ◽  
Single Image ◽  
Average Precision ◽  
Consistency Model ◽  
Head Detection

Head detection in real-world videos is a classical research problem in computer vision. Head detection in videos is challenging than in a single image due to many nuisances that are commonly observed in natural videos, including arbitrary poses, appearances, and scales. Generally, head detection is treated as a particular case of object detection in a single image. However, the performance of object detectors deteriorates in unconstrained videos. In this paper, we propose a temporal consistency model (TCM) to enhance the performance of a generic object detector by integrating spatial-temporal information that exists among subsequent frames of a particular video. Generally, our model takes detection from a generic detector as input and improves mean average precision (mAP) by recovering missed detection and suppressing false positives. We compare and evaluate the proposed framework on four challenging datasets, i.e., HollywoodHeads, Casablanca, BOSS, and PAMELA. Experimental evaluation shows that the performance is improved by employing the proposed TCM model. We demonstrate both qualitatively and quantitatively that our proposed framework obtains significant improvements over other methods.

scholarly journals DWCA-YOLOv5: An Improve Single Shot Detector for Safety Helmet Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhang Jin ◽  
Peiqi Qu ◽  
Cheng Sun ◽  
Meng Luo ◽  
Yan Gui ◽  
...  
Keyword(s):  
Information Dissemination ◽  
Detection Efficiency ◽  
A Priori ◽  
Detection Methods ◽  
Single Shot ◽  
Accuracy Rate ◽  
Average Accuracy ◽  
Head Detection ◽  
Matching Degree ◽  
Complex Construction

Aiming at solving the problem that the detection methods used in the existing helmet detection research has low detection efficiency and the cumulative error influences accuracy, a new algorithm for improving YOLOv5 helmet wearing detection is proposed. First of all, we use the K -means++ algorithm to improve the size matching degree of the a priori anchor box; secondly, integrate the Depthwise Coordinate Attention (DWCA) mechanism in the backbone network, so that the network can learn the weight of each channel independently and enhance the information dissemination between features, thereby strengthening the network’s ability to distinguish foreground and background. The experimental results show as follows: in the self-made safety helmet wearing detection dataset, the average accuracy rate reached 95.9%, the average accuracy of the helmet detection reached 96.5%, and the average accuracy of the worker’s head detection reached 95.2%. Making a comparison with the YOLOv5 algorithm, our model has a 3% increase in the average accuracy of helmet detection, which is in line with the accuracy requirements of helmet wearing detection in complex construction scenarios.

scholarly journals FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2440
Author(s):  
Faris A. Kateb ◽  
Muhammad Mostafa Monowar ◽  
Md. Abdul Hamid ◽  
Abu Quwsar Ohi ◽  
Muhammad Firoz Mridha
Keyword(s):  
Computer Vision ◽  
Learning Strategies ◽  
Precision Agriculture ◽  
Detection System ◽  
Detection Methods ◽  
High Complexity ◽  
Detection Mechanism ◽  
Detection Model ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible to embed in an automated device. Nevertheless, most investigation of fruit detection is limited to a single fruit, resulting in the necessity of a one-to-many object detection system. This paper introduces a generic detection mechanism named FruitDet, designed to be prominent for detecting fruits. The FruitDet architecture is designed on the YOLO pipeline and achieves better performance in detecting fruits than any other detection model. The backbone of the detection model is implemented using DenseNet architecture. Further, the FruitDet is packed with newer concepts: attentive pooling, bottleneck spatial pyramid pooling, and blackout mechanism. The detection mechanism is benchmarked using five datasets, which combines a total of eight different fruit classes. The FruitDet architecture acquires better performance than any other recognized detection methods in fruit detection.

scholarly journals Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Etienne David ◽  
Simon Madec ◽  
Pouria Sadeghi-Tehran ◽  
Helge Aasen ◽  
Bangyou Zheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Head Orientation ◽  
Growth Stages ◽  
Maturity Stage ◽  
Genotypic Differences ◽  
Wide Range ◽  
Head Detection ◽  
Fair Principles

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

scholarly journals A Novel Regional Fusion Network for 3D Object Detection based on RGB Images and Point Clouds

2021 ◽  
Author(s):  
Hung-Hao Chen ◽  
Chia-Hung Wang ◽  
Hsueh-Wei Chen ◽  
Pei-Yung Hsiao ◽  
Li-Chen Fu ◽  
...  
Keyword(s):  
Object Detection ◽  
Receptive Fields ◽  
Point Clouds ◽  
Detection Methods ◽  
Lidar Data ◽  
3D Object ◽  
Multi Scale ◽  
Interest Level ◽  
Rgb Images ◽  
3D Object Detection

The current fusion-based methods transform LiDAR data into bird’s eye view (BEV) representations or 3D voxel, leading to information loss and heavy computation cost of 3D convolution. In contrast, we directly consume raw point clouds and perform fusion between two modalities. We employ the concept of region proposal network to generate proposals from two streams, respectively. In order to make two sensors compensate the weakness of each other, we utilize the calibration parameters to project proposals from one stream onto the other. With the proposed multi-scale feature aggregation module, we are able to combine the extracted regionof-interest-level (RoI-level) features of RGB stream from different receptive fields, resulting in fertilizing feature richness. Experiments on KITTI dataset show that our proposed network outperforms other fusion-based methods with meaningful improvements as compared to 3D object detection methods under challenging setting.

Improved Background Subtraction Technique for detecting Moving Object

2020 ◽  
Vol 13 ◽  
Author(s):  
Tannistha Pal
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Image Enhancement ◽  
Background Subtraction ◽  
Moving Object ◽  
Detection Methods ◽  
Subtraction Method ◽  
Subtraction Technique ◽  
Visual Background ◽  
Computer Vision Applications

Introduction: Moving object detection from videos is among the most difficult task in different areas of computer vision applications. Among the traditional object detection methods, researchers conclude that Background Subtraction method carried out better in aspects of execution time and output quality. Mehtod: Visual background extractor is a renowned algorithm in Background Subtraction method for detecting moving object in various applications. In the recent years, lots of work has been carried out to improve the existing Visual Background extractor algorithm. Result: After investigating many state of art techniques and finding out the research gaps, this paper presents an improved background subtraction technique based on morphological operation and 2D median filter for detecting moving object which reduces the noise in the output video and also enhances its accuracy at a very limited additional cost. Experimental results in several benchmark datasets confirmed the superiority of the proposed method over the state-of-the-art object detection methods. Conclusion: In this article, a method has been proposed for moving object detection where the quality of the output object is enhanced and good accuracy is achieved. This method provide with accurate experimental results, which helps in efficient object detection. The proposed technique also deals with Visual Background extractor Algorithm along with the Image Enhancement Procedure like Morphological and 2-D Filtering at a limited additional cost Discussion: This article worked on certain specific field, like noise reduction and image enhancement of output images of the existing ViBe Algorithm. The technique proposed in this article will be beneficial for various computer vision applications like video surveillance, road condition monitoring, airport safety, human activity analysis, monitoring marine border for security purpose etc.

scholarly journals Real-Time Detection for Wheat Head Applying Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 191
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Detection Methods ◽  
Convolutional Network ◽  
Head Detection ◽  
Real Time Detection ◽  
Low Efficiency ◽  
Basic Network

Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3′s head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection.

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