Research on Intelligent Trash Can Garbage Classification Scheme Based on Improved YOLOv3 Target Detection Algorithm

2021 ◽  
Author(s):  
Ying Wang ◽  
BalaAnand Muthu ◽  
M. Anbarasan
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Assistive Technology ◽  
Target Detection ◽  
Classification Accuracy ◽  
Classification Scheme ◽  
Detection Algorithm ◽  
The Disabled ◽  
Classification Evaluation ◽  
Visually Disabled

In recent studies, YOLOv3, a deep learning-based target detection algorithm, becomes extensively used in object recognition, especially guiding the visually disabled. Current YOLOv3-based assistive technology for the disabled person can now achieve high-precision, real-time object recognition. Even though this algorithm has several flaws, including the failure to estimate distances and the difficulty of accurately recognizing points in fog or haze, it can perform well in waste management. Therefore, this study proposes an Intelligent Garbage Monitoring Scheme based on an improved YOLOv3 Target Detection Algorithm (IGMS-iYTDA) to classify the IoT’sgarbages (IoT) enabled trash can. The performance of the proposed scheme has been evaluated and illustrated for various classification evaluation metrics. The evaluation results show the highest classification accuracy of 99.9% compared to existing models for the proposed scheme.

scholarly journals A New Steel Defect Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weidong Zhao ◽  
Feng Chen ◽  
Hancheng Huang ◽  
Dan Li ◽  
Wei Cheng
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Average Precision ◽  
Original Algorithm ◽  
Surface Defect Detection

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

scholarly journals A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yi Lv ◽  
Zhengbo Yin ◽  
Zhezhou Yu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Target Detection ◽  
Detection Efficiency ◽  
False Positive Rate ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Data Set ◽  
Detection Effect

In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segmentation detection are designed. The data set used in the experiment comes from GoogleEarth, and there are 6 types of objects: airplanes, boats, warehouses, large ships, bridges, and ports. Training set, verification set, and test set contain 73490 images, 22722 images, and 2138 images, respectively. It is assumed that the number of detected positive samples and negative samples is A and B, respectively, and the number of undetected positive samples and negative samples is C and D, respectively. The experimental results show that the precision-recall curve of DFS for six types of targets shows that DFS has the best detection effect for bridges and the worst detection effect for boats. The main reason is that the size of the bridge is relatively large, and it is clearly distinguished from the background in the image, so the detection difficulty is low. However, the target of the boat is very small, and it is easy to be mixed with the background, so it is difficult to detect. The MAP of DFS is improved by 12.82%, the detection accuracy is improved by 13%, and the recall rate is slightly decreased by 1% compared with YOLOv2. According to the number of detection targets, the number of false positives (FPs) of DFS is much less than that of YOLOv2. The false positive rate is greatly reduced. In addition, the average IOU of DFS is 11.84% higher than that of YOLOv2. For small target detection efficiency and large remote sensing image detection, the DFS algorithm has obvious advantages.

scholarly journals Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Template Matching ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Information Warfare ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

scholarly journals Real Time Object Recognition and Classification using Deep Learning

2019 ◽  
pp. 355-359
Author(s):  
Kiruthiga N ◽  
Divya E ◽  
Haripriya R ◽  
Haripriya V.
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Object Detection ◽  
Real Time ◽  
Visually Impaired ◽  
Detection Algorithm ◽  
Indoor Environments ◽  
Wide Range ◽  
Impaired Person ◽  
First Time

Navigation in indoor environments is highly challenging for visually impaired person, particularly in spaces visited for the first time. Various solutions have been proposed to deal with this challenge. In this project consider as the real time object Recognition and classification using deep learning algorithms. Object detection mainly deals with identification of real time objects such as people, animals, and objects. Object detection algorithm uses a wide range of image processing applications for extracting the object's desired portion. This enables one to identify the objects and calculate the accuracy of the object and deliver through voice. Using this information, the system determines the user's trajectory and can locate possible obstacles in that route.

Sign in / Sign up

Export Citation Format

Share Document