Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network
ABSTRACT: Detecting and classifying animal species is the first step in determining their long-term viability and the influence we may be having on them. Second, it aids people in recognizing predators and non-predatory animals, both of which pose a significant threat to humans and the environment....
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-07-01
|
| Series: | Scientific African |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227622000606 |
| _version_ | 1797996643628351488 |
|---|---|
| author | Chiagoziem C. Ukwuoma Zhiguang Qin Sophyani B. Yussif Monday N. Happy Grace U. Nneji Gilbert C. Urama Chibueze D. Ukwuoma Nimo B. Darkwa Harriet Agobah |
| author_facet | Chiagoziem C. Ukwuoma Zhiguang Qin Sophyani B. Yussif Monday N. Happy Grace U. Nneji Gilbert C. Urama Chibueze D. Ukwuoma Nimo B. Darkwa Harriet Agobah |
| author_sort | Chiagoziem C. Ukwuoma |
| collection | DOAJ |
| description | ABSTRACT: Detecting and classifying animal species is the first step in determining their long-term viability and the influence we may be having on them. Second, it aids people in recognizing predators and non-predatory animals, both of which pose a significant threat to humans and the environment. Third, it lowers the rate of traffic accidents in various regions since it has been a regular sighting on roadways, resulting in several collisions with automobiles. However, animal species' detection and Classification of animal species face many challenges such as the size and inconsistent behaviors various among the species. This paper proposes using a novel two-stage network with a modified multi-scale attention mechanism to create a more integrated recognition and classification system to attend to the challenges. At the regional proposal stage, a deeply characterized pyramid design with lateral connections was adopted, making the semantic characteristic of a small item more sensitive. Secondly, by reason of a densely connected convolutional network, the functional transmission is enhanced and multiplexed throughout the classification stage, resulting in a more precise Classification with fewer parameters. The Proposed model was evaluated using the AP and mAP evaluation metrics on the Animal wildlife and the challenging Animal-80 dataset. An mAP of +0.1% and an AP of 5% to 20% increase in each class was achieved by the attention-based proposed model compared to the non-attention-based model. Further comparison with other related works shows the proposed techniques' effectiveness for detecting and classifying animal species. |
| first_indexed | 2024-04-11T10:20:39Z |
| format | Article |
| id | doaj.art-8dfd890a632e40a49f45fdf062cc4625 |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-04-11T10:20:39Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-8dfd890a632e40a49f45fdf062cc46252022-12-22T04:29:46ZengElsevierScientific African2468-22762022-07-0116e01151Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid networkChiagoziem C. Ukwuoma0Zhiguang Qin1Sophyani B. Yussif2Monday N. Happy3Grace U. Nneji4Gilbert C. Urama5Chibueze D. Ukwuoma6Nimo B. Darkwa7Harriet Agobah8School of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan PR China; Corresponding author.School of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan PR China; Corresponding author.School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan PR ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan PR ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan PR ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan PR ChinaDepartmemt of Physics-Electronics, Federal University of Technology Owerri, Nigeria.School of Computer Science and Engineering, University of Electronic Science and Technology of China, Sichuan PR ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan PR ChinaABSTRACT: Detecting and classifying animal species is the first step in determining their long-term viability and the influence we may be having on them. Second, it aids people in recognizing predators and non-predatory animals, both of which pose a significant threat to humans and the environment. Third, it lowers the rate of traffic accidents in various regions since it has been a regular sighting on roadways, resulting in several collisions with automobiles. However, animal species' detection and Classification of animal species face many challenges such as the size and inconsistent behaviors various among the species. This paper proposes using a novel two-stage network with a modified multi-scale attention mechanism to create a more integrated recognition and classification system to attend to the challenges. At the regional proposal stage, a deeply characterized pyramid design with lateral connections was adopted, making the semantic characteristic of a small item more sensitive. Secondly, by reason of a densely connected convolutional network, the functional transmission is enhanced and multiplexed throughout the classification stage, resulting in a more precise Classification with fewer parameters. The Proposed model was evaluated using the AP and mAP evaluation metrics on the Animal wildlife and the challenging Animal-80 dataset. An mAP of +0.1% and an AP of 5% to 20% increase in each class was achieved by the attention-based proposed model compared to the non-attention-based model. Further comparison with other related works shows the proposed techniques' effectiveness for detecting and classifying animal species.http://www.sciencedirect.com/science/article/pii/S2468227622000606Deep LearningMultiscale Attention MechanismFeature pyramidAnimal Detectionand Classification |
| spellingShingle | Chiagoziem C. Ukwuoma Zhiguang Qin Sophyani B. Yussif Monday N. Happy Grace U. Nneji Gilbert C. Urama Chibueze D. Ukwuoma Nimo B. Darkwa Harriet Agobah Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network Scientific African Deep Learning Multiscale Attention Mechanism Feature pyramid Animal Detection and Classification |
| title | Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network |
| title_full | Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network |
| title_fullStr | Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network |
| title_full_unstemmed | Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network |
| title_short | Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network |
| title_sort | animal species detection and classification framework based on modified multi scale attention mechanism and feature pyramid network |
| topic | Deep Learning Multiscale Attention Mechanism Feature pyramid Animal Detection and Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2468227622000606 |
| work_keys_str_mv | AT chiagoziemcukwuoma animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT zhiguangqin animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT sophyanibyussif animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT mondaynhappy animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT graceunneji animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT gilbertcurama animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT chibuezedukwuoma animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT nimobdarkwa animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork AT harrietagobah animalspeciesdetectionandclassificationframeworkbasedonmodifiedmultiscaleattentionmechanismandfeaturepyramidnetwork |