Back to Search
Start Over
Real-World Field Snail Detection and Tracking
- Source :
- ICARCV
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- With the development of computer vision and machine learning, smart farming is becoming more popular and more important in agricultural industries. In this paper, we design and develop a snail detection and tracking system for real-world application. In this approach, deep learning is adopted to detect snails in the real-world. This can make full use of the computer's computing power to analyze big data and reduce researchers' workload. We have set up a snail dataset according to the video collected in the field environment, and we use a Faster R-CNN based algorithm to detect snails. Experiments show that this method can achieve good detection results. On this basis, by analyzing snail data sets, we optimized Faster R-CNN based algorithm according to the characteristics of snail's smaller size. These two methods are used by setting different anchor scale sizes and combining shallower features for detection. As a result, we improve the performance of snail detection in field conditions. We also adopt a linear Kalman filter as tracker to link objects into each trajectories Refereed/Peer-reviewed
- Subjects :
- Computer science
Big data
02 engineering and technology
Snail
010501 environmental sciences
Tracking (particle physics)
computer.software_genre
01 natural sciences
Field (computer science)
Set (abstract data type)
biology.animal
0202 electrical engineering, electronic engineering, information engineering
computer vision and machine learning
0105 earth and related environmental sciences
biology
snail detection
business.industry
Deep learning
agricultural industries
Tracking system
020201 artificial intelligence & image processing
Data mining
Artificial intelligence
business
Scale (map)
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)
- Accession number :
- edsair.doi.dedup.....255edb5a52723c00d8bfbff15f204e4a