Real-time object detection using IoT devices : detecting rodents and birds for conservation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand
| dc.contributor.author | Shim, Kyuwon | |
| dc.date.accessioned | 2022-04-05T02:58:29Z | |
| dc.date.available | 2022-04-05T02:58:29Z | |
| dc.date.issued | 2021 | |
| dc.description | Figure 2.23 (=Misra et al., 2020 Fig 19.17) was removed for copyright reasons. Figures 2.4 © 2017 IEEE, 2.7 © 2018 IEEE, 2.8 © 2016 IEEE, 2.9 © 2016 IEEE & 2.10 © 2018 IEEE are used with permission. Figures 2.15, 2.18, 2.19, 2.22, 2.25 & 2.26 are re-used under various Creative Commons (CC-BY) licences. | en |
| dc.description.abstract | In New Zealand forests rodents currently threatens various species of native birds. Unfortunately, the traditional traps and poisoning are not sufficient to eliminate an increasing number of rodents in our forests. In this work we developed an auxiliary control tool that combines the latest computer technology, such as object recognition algorithms, with Internet of Things(IoT) devices. A drawback of most object recognition algorithms is that it requires a tremendous amount of computation, so these techniques are not necessarily adequate to be applied to IoT devices. This thesis proposes to create a real-time object detection algorithm using IoT devices for two classes of objects, rodents and birds. Two methods were implemented and tested. The first method is a shape-based algorithm using Fourier Descriptors (FD). In order to find the ideal classification algorithm for the FD method, we compared the accuracies of four different classification algorithms: Neural Network, Random Forest, AdaBoost, and Support Vector Machine(SVM). The second method is based on YOLO (You Only Look Once). As YOLO requires a huge amount of images to train well, we created a semi-automated labelling system in our detection algorithm to boost the number of images for training. The labelling system produces the training requirements for both Yolo and FD and promises much faster processing time than manual labelling work. The labelling system succeeded in obtaining the labelled data of 2172 rodents and 3494 birds. Using a desktop machine and the FD method, and the average accuracy rate was 80% when using the Random Forest classifier. Using a desktop machine and the YOLO method, the average accuracy rate was 97%. The frame rates were 19 fps for the FD, 3.7 fps for the YOLO on CPUs and 90.9 fps for YOLO running on a GPU. Using a Raspberry Pi 3 and a video dataset, the FD method resulted in an 83% general accuracy and a frame rate of 6.33 fps, which is 30% more accurate and 21 times faster than YOLO. The experiments showed the feasibility of using the FD method in Raspberry Pis for real-time detection, with performance advantages compared to the YOLO architecture. | en |
| dc.identifier.uri | http://hdl.handle.net/10179/17011 | |
| dc.language.iso | en | en |
| dc.publisher | Massey University | en |
| dc.rights | The Author | en |
| dc.subject.anzsrc | 460303 Computational imaging | en |
| dc.title | Real-time object detection using IoT devices : detecting rodents and birds for conservation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand | en |
| dc.type | Thesis | en |
| massey.contributor.author | Shim, Kyuwon | |
| thesis.degree.discipline | Computer Science | en |
| thesis.degree.grantor | Massey University | en |
| thesis.degree.level | Masters | en |
| thesis.degree.name | Master of Science (MSc) | en |