Comparison of different deep learning object detection algorithms on fruit drying characterization
Object detection is an essential task in the field of computer vision and a prominent area of research. In the past, the categorization of raw and dry Tamanu fruits was dependent on human perception. Nevertheless, due to the progress in object detection, this task can currently be computerized. This...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Sdn. Bhd.
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43567/ http://umpir.ump.edu.my/id/eprint/43567/1/Comparison%20of%20different%20deep%20learning%20object%20detection%20algorithms%20on%20fruit%20drying%20characterization.pdf |
| _version_ | 1848826906421493760 |
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| author | Umair, Mohammad Yamin Norazlianie, Sazali Kettner, Maurice Mohd Azraai, Mohd Razman Weiβ, Robert |
| author_facet | Umair, Mohammad Yamin Norazlianie, Sazali Kettner, Maurice Mohd Azraai, Mohd Razman Weiβ, Robert |
| author_sort | Umair, Mohammad Yamin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Object detection is an essential task in the field of computer vision and a prominent area of research. In the past, the categorization of raw and dry Tamanu fruits was dependent on human perception. Nevertheless, due to the progress in object detection, this task can currently be computerized. This study employs three deep learning object detection models: You Only Look Once v5m (YOLOv5m), Single Shot Detector (SSD) MobileNet and EfficientDet. The models were trained using images of Tamanu fruits in their raw and dry state, which were directly collected from the dryer device. Following the completion of training, the models underwent evaluation to identify the one with the highest level of accuracy. YOLOv5m demonstrated superior performance compared to SSD MobileNet and EfficientDet, achieving a mean average precision (mAP) of 0.99589. SSD MobileNet demonstrated exceptional performance in real-time object detection, accurately detecting the majority of objects with a high level of confidence. This study showcases the efficacy of employing deep learning object detection models to automate the classification of Tamanu fruit. |
| first_indexed | 2025-11-15T03:52:16Z |
| format | Article |
| id | ump-43567 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:52:16Z |
| publishDate | 2024 |
| publisher | Semarak Ilmu Sdn. Bhd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-435672025-01-12T10:13:01Z http://umpir.ump.edu.my/id/eprint/43567/ Comparison of different deep learning object detection algorithms on fruit drying characterization Umair, Mohammad Yamin Norazlianie, Sazali Kettner, Maurice Mohd Azraai, Mohd Razman Weiβ, Robert TS Manufactures Object detection is an essential task in the field of computer vision and a prominent area of research. In the past, the categorization of raw and dry Tamanu fruits was dependent on human perception. Nevertheless, due to the progress in object detection, this task can currently be computerized. This study employs three deep learning object detection models: You Only Look Once v5m (YOLOv5m), Single Shot Detector (SSD) MobileNet and EfficientDet. The models were trained using images of Tamanu fruits in their raw and dry state, which were directly collected from the dryer device. Following the completion of training, the models underwent evaluation to identify the one with the highest level of accuracy. YOLOv5m demonstrated superior performance compared to SSD MobileNet and EfficientDet, achieving a mean average precision (mAP) of 0.99589. SSD MobileNet demonstrated exceptional performance in real-time object detection, accurately detecting the majority of objects with a high level of confidence. This study showcases the efficacy of employing deep learning object detection models to automate the classification of Tamanu fruit. Semarak Ilmu Sdn. Bhd. 2024 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/43567/1/Comparison%20of%20different%20deep%20learning%20object%20detection%20algorithms%20on%20fruit%20drying%20characterization.pdf Umair, Mohammad Yamin and Norazlianie, Sazali and Kettner, Maurice and Mohd Azraai, Mohd Razman and Weiβ, Robert (2024) Comparison of different deep learning object detection algorithms on fruit drying characterization. Journal of Advanced Research in Applied Sciences and Engineering Technology. pp. 1-14. ISSN 2462-1943. (In Press / Online First) (In Press / Online First) https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/12160 |
| spellingShingle | TS Manufactures Umair, Mohammad Yamin Norazlianie, Sazali Kettner, Maurice Mohd Azraai, Mohd Razman Weiβ, Robert Comparison of different deep learning object detection algorithms on fruit drying characterization |
| title | Comparison of different deep learning object detection algorithms on fruit drying characterization |
| title_full | Comparison of different deep learning object detection algorithms on fruit drying characterization |
| title_fullStr | Comparison of different deep learning object detection algorithms on fruit drying characterization |
| title_full_unstemmed | Comparison of different deep learning object detection algorithms on fruit drying characterization |
| title_short | Comparison of different deep learning object detection algorithms on fruit drying characterization |
| title_sort | comparison of different deep learning object detection algorithms on fruit drying characterization |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/43567/ http://umpir.ump.edu.my/id/eprint/43567/ http://umpir.ump.edu.my/id/eprint/43567/1/Comparison%20of%20different%20deep%20learning%20object%20detection%20algorithms%20on%20fruit%20drying%20characterization.pdf |