Camouflaged Insect Segmentation Using a Progressive Refinement Network
Accurately segmenting an insect from its original ecological image is the core technology restricting the accuracy and efficiency of automatic recognition. However, the performance of existing segmentation methods is unsatisfactory in insect images shot in wild backgrounds on account of challenges:...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/4/804 |
| _version_ | 1797621381673779200 |
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| author | Jing Wang Minglin Hong Xia Hu Xiaolin Li Shiguo Huang Rong Wang Feiping Zhang |
| author_facet | Jing Wang Minglin Hong Xia Hu Xiaolin Li Shiguo Huang Rong Wang Feiping Zhang |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Accurately segmenting an insect from its original ecological image is the core technology restricting the accuracy and efficiency of automatic recognition. However, the performance of existing segmentation methods is unsatisfactory in insect images shot in wild backgrounds on account of challenges: various sizes, similar colors or textures to the surroundings, transparent body parts and vague outlines. These challenges of image segmentation are accentuated when dealing with camouflaged insects. Here, we developed an insect image segmentation method based on deep learning termed the progressive refinement network (PRNet), especially for camouflaged insects. Unlike existing insect segmentation methods, PRNet captures the possible scale and location of insects by extracting the contextual information of the image, and fuses comprehensive features to suppress distractors, thereby clearly segmenting insect outlines. Experimental results based on 1900 camouflaged insect images demonstrated that PRNet could effectively segment the camouflaged insects and achieved superior detection performance, with a mean absolute error of 3.2%, pixel-matching degree of 89.7%, structural similarity of 83.6%, and precision and recall error of 72%, which achieved improvements of 8.1%, 25.9%, 19.5%, and 35.8%, respectively, when compared to the recent salient object detection methods. As a foundational technology for insect detection, PRNet provides new opportunities for understanding insect camouflage, and also has the potential to lead to a step progress in the accuracy of the intelligent identification of general insects, and even being an ultimate insect detector. |
| first_indexed | 2024-03-11T08:55:11Z |
| format | Article |
| id | doaj.art-ad1435452eba4f3aa98cf022eefb39f1 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T08:55:11Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ad1435452eba4f3aa98cf022eefb39f12023-11-16T20:10:18ZengMDPI AGElectronics2079-92922023-02-0112480410.3390/electronics12040804Camouflaged Insect Segmentation Using a Progressive Refinement NetworkJing Wang0Minglin Hong1Xia Hu2Xiaolin Li3Shiguo Huang4Rong Wang5Feiping Zhang6College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaAccurately segmenting an insect from its original ecological image is the core technology restricting the accuracy and efficiency of automatic recognition. However, the performance of existing segmentation methods is unsatisfactory in insect images shot in wild backgrounds on account of challenges: various sizes, similar colors or textures to the surroundings, transparent body parts and vague outlines. These challenges of image segmentation are accentuated when dealing with camouflaged insects. Here, we developed an insect image segmentation method based on deep learning termed the progressive refinement network (PRNet), especially for camouflaged insects. Unlike existing insect segmentation methods, PRNet captures the possible scale and location of insects by extracting the contextual information of the image, and fuses comprehensive features to suppress distractors, thereby clearly segmenting insect outlines. Experimental results based on 1900 camouflaged insect images demonstrated that PRNet could effectively segment the camouflaged insects and achieved superior detection performance, with a mean absolute error of 3.2%, pixel-matching degree of 89.7%, structural similarity of 83.6%, and precision and recall error of 72%, which achieved improvements of 8.1%, 25.9%, 19.5%, and 35.8%, respectively, when compared to the recent salient object detection methods. As a foundational technology for insect detection, PRNet provides new opportunities for understanding insect camouflage, and also has the potential to lead to a step progress in the accuracy of the intelligent identification of general insects, and even being an ultimate insect detector.https://www.mdpi.com/2079-9292/12/4/804camouflaged insectsdeep learninginsect detectionobject segmentationprogressive refinement network |
| spellingShingle | Jing Wang Minglin Hong Xia Hu Xiaolin Li Shiguo Huang Rong Wang Feiping Zhang Camouflaged Insect Segmentation Using a Progressive Refinement Network Electronics camouflaged insects deep learning insect detection object segmentation progressive refinement network |
| title | Camouflaged Insect Segmentation Using a Progressive Refinement Network |
| title_full | Camouflaged Insect Segmentation Using a Progressive Refinement Network |
| title_fullStr | Camouflaged Insect Segmentation Using a Progressive Refinement Network |
| title_full_unstemmed | Camouflaged Insect Segmentation Using a Progressive Refinement Network |
| title_short | Camouflaged Insect Segmentation Using a Progressive Refinement Network |
| title_sort | camouflaged insect segmentation using a progressive refinement network |
| topic | camouflaged insects deep learning insect detection object segmentation progressive refinement network |
| url | https://www.mdpi.com/2079-9292/12/4/804 |
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