scholarly journals Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery

2021 ◽  
Vol 13 (24) ◽  
pp. 5182
Author(s):  
Aaron Etienne ◽  
Aanis Ahmad ◽  
Varun Aggarwal ◽  
Dharmendra Saraswat
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Weed Management ◽  
Detection System ◽  
Ground Level ◽  
Training Image ◽  
Field Conditions ◽  
Herbicide Application ◽  
Weed Detection ◽  
Management Procedures

Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures for targeted herbicide application within a field. However, a major challenge to developing a weed detection system is the requirement for a properly annotated database to differentiate between weeds and crops under field conditions. This research involved creating an annotated database of 374 red, green, and blue (RGB) color images organized into monocot and dicot weed classes. The images were acquired from corn and soybean research plots located in north-central Indiana using an unmanned aerial system (UAS) flown at 30 and 10 m heights above ground level (AGL). A total of 25,560 individual weed instances were manually annotated. The annotated database consisted of four different subsets (Training Image Sets 1–4) to train the You Only Look Once version 3 (YOLOv3) deep learning model for five separate experiments. The best results were observed with Training Image Set 4, consisting of images acquired at 10 m AGL. For monocot and dicot weeds, respectively, an average precision (AP) score of 91.48 % and 86.13% was observed at a 25% IoU threshold (AP @ T = 0.25), as well as 63.37% and 45.13% at a 50% IoU threshold (AP @ T = 0.5). This research has demonstrated a need to develop large, annotated weed databases to evaluate deep learning models for weed identification under field conditions. It also affirms the findings of other limited research studies utilizing object detection for weed identification under field conditions.

scholarly journals Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

2018 ◽  
Author(s):  
Jesus Benito-Picazo ◽  
Enrique Dominguez ◽  
Esteban J. Palomo ◽  
Ezequiel Lopez-Rubio ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Detection System

scholarly journals Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery

2020 ◽  
Vol 12 (13) ◽  
pp. 2136 ◽  
Author(s):  
Arun Narenthiran Veeranampalayam Sivakumar ◽  
Jiating Li ◽  
Stephen Scott ◽  
Eric Psota ◽  
Amit J. Jhala ◽  
...  
Keyword(s):  
Object Detection ◽  
Weed Management ◽  
Network Models ◽  
Detection Performance ◽  
Single Shot ◽  
Neural Network Models ◽  
Weed Detection ◽  
Late Season ◽  
Detection Model ◽  
Growing Seasons

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box proposals had similar good weed detection performance to the SSD model in terms of precision, recall, f1 score, and IoU, as well as a similar inference time. The precision, recall, f1 score and IoU were 0.65, 0.68, 0.66 and 0.85 for Faster RCNN with 200 proposals, and 0.66, 0.68, 0.67 and 0.84 for SSD, respectively. However, the optimal confidence threshold of the SSD model was found to be much lower than that of the Faster RCNN model, which indicated that SSD might have lower generalization performance than Faster RCNN for mid- to late-season weed detection in soybean fields using UAV imagery. The performance of the object detection model was also compared with patch-based CNN model. The Faster RCNN model yielded a better weed detection performance than the patch-based CNN with and without overlap. The inference time of Faster RCNN was similar to patch-based CNN without overlap, but significantly less than patch-based CNN with overlap. Hence, Faster RCNN was found to be the best model in terms of weed detection performance and inference time among the different models compared in this study. This work is important in understanding the potential and identifying the algorithms for an on-farm, near real-time weed detection and management.

scholarly journals Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021 ◽  
Vol 7 (9) ◽  
pp. 176
Author(s):  
Daniel Queirós da Silva ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Vítor Filipe
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Forest Inventory ◽  
Mobile Robotics ◽  
Ground Level ◽  
Forest Tree ◽  
Thermal Image ◽  
Detection Methods ◽  
Site Management ◽  
Forest Wildfires

Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

2020 ◽  
Vol 12 (1) ◽  
pp. 182 ◽  
Author(s):  
Lingxuan Meng ◽  
Zhixing Peng ◽  
Ji Zhou ◽  
Jirong Zhang ◽  
Zhenyu Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Detection System ◽  
Detection Model ◽  
Aerial Vehicle ◽  
Real Time Detection ◽  
Pipeline Safety

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

scholarly journals Brand Detection System using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 497-500
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Visual Representation ◽  
Cosmetic Products ◽  
Medical Object

In this paper a method of recognizing logos of the brand of cosmetic products using deep learning. There are several of hoax product which easily copies the famous brand’s logo and deteriorates the company’s image. The machine learning has proved to be useful in various of the fields like medical, object detection, vehicle logo recognitions. But till now very few of the works have been performed in cosmetic field. This field is covered using the model sequential convolutional neural network using Tensorflow and Keras. For the visual representation of the result Tensorboard is used. Work have been started with two of the brands-Lakme and L’Oreal. Depending upon the success of this technique, further brands for logo may be added for recognition. The accuracy of approximately 80% was obtained using this technique.

scholarly journals OBJECT DETECTION SYSTEM FOR THE BLIND WITH VOICEGUIDANCE

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Rajeshvaree Ravindra Karmarkar ◽  
Prof.V.N Honmane
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Object Detection ◽  
Visually Impaired ◽  
Detection System ◽  
Learning Model ◽  
Learning Technologies ◽  
Industrial Facilities ◽  
Guidance Technique ◽  
Deep Learning Model

—As object recognition technology has developed recently, various technologies have been applied to autonomousvehicles, robots, and industrial facilities. However, the benefits ofthese technologies are not reaching the visually impaired, who need it the most. This paper proposed an object detection system for the blind using deep learning technologies. Furthermore, a voice guidance technique is used to inform sight impaired persons as to the location of objects. The object recognition deep learning model utilizes the You Only Look Once(YOLO) algorithm and a voice announcement is synthesized using text-tospeech (TTS) to make it easier for the blind to get information about objects. Asa result, it implements an efficient object-detection system that helps the blind find objects in a specific space without help from others, and the system is analyzed through experiments to verify performance.

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