Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion

The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy is low in a complex natural environment. In this paper, an object detection algorithm based on deep learning was proposed for sugarcane stem node recognition in a complex natural environment, and the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The impact of the data expansion and lighting condition in different time periods on the results of sugarcane stem nodes detection was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparing it with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The comparison results showed that the AP (average precision) of the sugarcane stem nodes detected by YOLO v4 was 95.17%, which was higher than that of the other four algorithms (78.87%, 88.98%, 90.88% and 92.69%, respectively). Meanwhile, the detection speed of the YOLO v4 method was 69 f/s and exceeded the requirement of a real-time detection speed of 30 f/s. The research shows that it is a feasible method for real-time detection of sugarcane stem nodes in a complex natural environment. This research provides visual technical support for the development of intelligent sugarcane harvesters.

Download Full-text

Deep Learning Approach for high Energy efficient Real-Time Detection of Weeds in Organic Farming

2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) ◽

10.1109/i2mtc50364.2021.9459943 ◽

2021 ◽

Author(s):

Vitali Czymmek ◽

Clarissa Moller ◽

Leif O. Harders ◽

Stephan Hussmann

Keyword(s):

Deep Learning ◽

Real Time ◽

Organic Farming ◽

Energy Efficient ◽

High Energy ◽

Learning Approach ◽

Real Time Detection

Download Full-text

Real- Time Detection of Critical Generators in Power Systems: A Deep Learning HCP Approach

2020 IEEE Texas Power and Energy Conference (TPEC) ◽

10.1109/tpec48276.2020.9042552 ◽

2020 ◽

Cited By ~ 1

Author(s):

Bhavesh Shinde ◽

Shiyuan Wang ◽

Payman Dehghanian ◽

Mohammad Babakmehr

Keyword(s):

Deep Learning ◽

Power Systems ◽

Real Time ◽

Real Time Detection

Download Full-text

Real Time Detection of Speed Hump/Bump and Distance Estimation with Deep Learning using GPU and ZED Stereo Camera

Procedia Computer Science ◽

10.1016/j.procs.2018.10.335 ◽

2018 ◽

Vol 143 ◽

pp. 988-997 ◽

Cited By ~ 12

Author(s):

V S K P Varma ◽

S Adarsh ◽

K I Ramachandran ◽

Binoy B Nair

Keyword(s):

Deep Learning ◽

Real Time ◽

Distance Estimation ◽

Stereo Camera ◽

Real Time Detection

Download Full-text

Artificial Intelligence-Enabled Traffic Monitoring System

Sustainability ◽

10.3390/su12219177 ◽

2020 ◽

Vol 12 (21) ◽

pp. 9177

Author(s):

Vishal Mandal ◽

Abdul Rashid Mussah ◽

Peng Jin ◽

Yaw Adu-Gyamfi

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Real Time ◽

Monitoring System ◽

Traffic Management ◽

Traffic Monitoring ◽

Deep Convolutional Neural Networks ◽

Stage Of Development ◽

Daunting Task ◽

The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

Download Full-text