Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion
To enhance the real-time detection accuracy of ship exhaust plumes and further quantify the degree of darkness, this study proposes a multi-feature fusion approach that combines the YOLOv5s-CMBI algorithm for ship exhaust plume detection with the Ringerman Blackness-based grading method. Firstly, di...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/10/1945 |
| _version_ | 1797573397788491776 |
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| author | Shipeng Wang Yang Han Mengmeng Yu Haiyan Wang Zhen Wang Guangzheng Li Haochen Yu |
| author_facet | Shipeng Wang Yang Han Mengmeng Yu Haiyan Wang Zhen Wang Guangzheng Li Haochen Yu |
| author_sort | Shipeng Wang |
| collection | DOAJ |
| description | To enhance the real-time detection accuracy of ship exhaust plumes and further quantify the degree of darkness, this study proposes a multi-feature fusion approach that combines the YOLOv5s-CMBI algorithm for ship exhaust plume detection with the Ringerman Blackness-based grading method. Firstly, diverse datasets are integrated and a subset of the data is subjected to standard optical model aerosolization to form a dataset for ship exhaust plume detection. Subsequently, building upon the YOLOv5s architecture, the CBAM convolutional attention mechanism is incorporated to augment the network’s focus on ship exhaust plume regions while suppressing irrelevant information. Simultaneously, inspired by the BiFPN structure with weighted bidirectional feature pyramids, a lightweight network named Tiny-BiFPN is devised to enable multi-path feature fusion. The Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to counteract the impact of feature scale disparities. The EIoU_Loss is employed as the localization loss function to enhance both regression accuracy and convergence speed of the model. Lastly, leveraging the k-means clustering algorithm, color information is mined through histogram analysis to determine clustering centers. The Mahalanobis distance is used to compute sample similarity, and the Ringerman Blackness-based method is employed to categorize darkness levels. Ship exhaust plume grades are estimated by computing a weighted average grayscale ratio between the effective exhaust plume region and the background region. Experimental results reveal that the proposed algorithm achieves improvements of approximately 3.8% in detection accuracy, 5.7% in recall rate, and 4.6% in mean average precision (mAP0.5) compared to the original model. The accuracy of ship exhaust plume darkness grading attains 92.1%. The methodology presented in this study holds significant implications for the establishment and application of future ship exhaust plume monitoring mechanisms. |
| first_indexed | 2024-03-10T21:09:06Z |
| format | Article |
| id | doaj.art-a36dbdac160b40298454ae901fbe9e02 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T21:09:06Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-a36dbdac160b40298454ae901fbe9e022023-11-19T16:58:57ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-10-011110194510.3390/jmse11101945Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature FusionShipeng Wang0Yang Han1Mengmeng Yu2Haiyan Wang3Zhen Wang4Guangzheng Li5Haochen Yu6School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSergeant College, Binzhou Polytechnic, Binzhou 256600, ChinaSergeant College, Binzhou Polytechnic, Binzhou 256600, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSergeant College, Binzhou Polytechnic, Binzhou 256600, ChinaNaval Architecture and Port Engineering College, Shandong Jiaotong University, Weihai 264200, ChinaBeiHai Rescue Bureau, Ministry of Transport, Yantai 264000, ChinaTo enhance the real-time detection accuracy of ship exhaust plumes and further quantify the degree of darkness, this study proposes a multi-feature fusion approach that combines the YOLOv5s-CMBI algorithm for ship exhaust plume detection with the Ringerman Blackness-based grading method. Firstly, diverse datasets are integrated and a subset of the data is subjected to standard optical model aerosolization to form a dataset for ship exhaust plume detection. Subsequently, building upon the YOLOv5s architecture, the CBAM convolutional attention mechanism is incorporated to augment the network’s focus on ship exhaust plume regions while suppressing irrelevant information. Simultaneously, inspired by the BiFPN structure with weighted bidirectional feature pyramids, a lightweight network named Tiny-BiFPN is devised to enable multi-path feature fusion. The Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to counteract the impact of feature scale disparities. The EIoU_Loss is employed as the localization loss function to enhance both regression accuracy and convergence speed of the model. Lastly, leveraging the k-means clustering algorithm, color information is mined through histogram analysis to determine clustering centers. The Mahalanobis distance is used to compute sample similarity, and the Ringerman Blackness-based method is employed to categorize darkness levels. Ship exhaust plume grades are estimated by computing a weighted average grayscale ratio between the effective exhaust plume region and the background region. Experimental results reveal that the proposed algorithm achieves improvements of approximately 3.8% in detection accuracy, 5.7% in recall rate, and 4.6% in mean average precision (mAP0.5) compared to the original model. The accuracy of ship exhaust plume darkness grading attains 92.1%. The methodology presented in this study holds significant implications for the establishment and application of future ship exhaust plume monitoring mechanisms.https://www.mdpi.com/2077-1312/11/10/1945ship black smokeYOLOv5s-CMBIRingerman Blacknessdeep learning |
| spellingShingle | Shipeng Wang Yang Han Mengmeng Yu Haiyan Wang Zhen Wang Guangzheng Li Haochen Yu Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion Journal of Marine Science and Engineering ship black smoke YOLOv5s-CMBI Ringerman Blackness deep learning |
| title | Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion |
| title_full | Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion |
| title_fullStr | Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion |
| title_full_unstemmed | Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion |
| title_short | Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion |
| title_sort | research on black smoke detection and class evaluation method for ships based on yolov5s cmbi multi feature fusion |
| topic | ship black smoke YOLOv5s-CMBI Ringerman Blackness deep learning |
| url | https://www.mdpi.com/2077-1312/11/10/1945 |
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