Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation
The development of port automation requires sensors to detect container movement. Vision sensors have recently received considerable attention and are being developed as AI advances, leading to various container motion detection methods. Faster-RCNN is a detection method that performs better precisi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023004206 |
| _version_ | 1811161847985864704 |
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| author | Steven Bandong Yul Yunazwin Nazaruddin Endra Joelianto |
| author_facet | Steven Bandong Yul Yunazwin Nazaruddin Endra Joelianto |
| author_sort | Steven Bandong |
| collection | DOAJ |
| description | The development of port automation requires sensors to detect container movement. Vision sensors have recently received considerable attention and are being developed as AI advances, leading to various container motion detection methods. Faster-RCNN is a detection method that performs better precision and recall than other methods. Nonetheless, the detectors are set using the Faster-RCNN default parameters. It is of interest to optimized its parameters for producing more accurate detectors for container detection tasks. Faster RCNN requires mixed integer optimization for its continuous and integer parameters. Efficient Modified Particle Swarm Optimization (EMPSO) offers a method to optimize integer parameter by evolutionary updating the space of each candidate solution but has high possibility stuck in the local minima due to rapid growth of Gbest and Pbest space. This paper proposes two modifications to improve EMPSO that could adapt to the current global solution. Firstly, the non-Gbest and Pbest total position spaces are made adaptive to changes according to the Gbest and Pbest position spaces. Second, a weighted multiobjective optimization for Faster-RCNN is proposed based on minimum loss, average loss, and gradient of loss to give priority scale. The integer EMPSO with adaptive changes to Gbest and Pbest position space is first tested on nine non-linear standard test functions to validate its performance, the results show performance improvement in finding global minimum compared to EMPSO. This tested algorithm is then applied to optimize Faster-RCNN with the weighted cost function, which uses 1300 container images to train the model and then tested on four videos of moving containers at seaports. The results produce better performances regarding the speed and achieving the optimal solution. This technique causes better minimum losses, average losses, intersection over union, confidence score, precision, and accuracy than the results of the default parameters. |
| first_indexed | 2024-04-10T06:20:39Z |
| format | Article |
| id | doaj.art-7f4c0a001fc8493daef18c350cf5e0f4 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:20:39Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-7f4c0a001fc8493daef18c350cf5e0f42023-03-02T05:00:30ZengElsevierHeliyon2405-84402023-02-0192e13213Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automationSteven Bandong0Yul Yunazwin Nazaruddin1Endra Joelianto2Engineering Physics Doctoral Program, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, Indonesia; University Center of Excellence on Artificial Intelligence for Vision, NLP and Big Data Analytics, Institut Teknologi Bandung, Bandung, IndonesiaInstrumentation and Control Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, Indonesia; National Center for Sustainable Transportation Technology, CRCS Building, 2nd Floor, Institut Teknologi Bandung, Bandung, IndonesiaUniversity Center of Excellence on Artificial Intelligence for Vision, NLP and Big Data Analytics, Institut Teknologi Bandung, Bandung, Indonesia; Instrumentation and Control Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, Indonesia; National Center for Sustainable Transportation Technology, CRCS Building, 2nd Floor, Institut Teknologi Bandung, Bandung, Indonesia; Corresponding author. Instrumentation and Control Research Group, Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, Indonesia.The development of port automation requires sensors to detect container movement. Vision sensors have recently received considerable attention and are being developed as AI advances, leading to various container motion detection methods. Faster-RCNN is a detection method that performs better precision and recall than other methods. Nonetheless, the detectors are set using the Faster-RCNN default parameters. It is of interest to optimized its parameters for producing more accurate detectors for container detection tasks. Faster RCNN requires mixed integer optimization for its continuous and integer parameters. Efficient Modified Particle Swarm Optimization (EMPSO) offers a method to optimize integer parameter by evolutionary updating the space of each candidate solution but has high possibility stuck in the local minima due to rapid growth of Gbest and Pbest space. This paper proposes two modifications to improve EMPSO that could adapt to the current global solution. Firstly, the non-Gbest and Pbest total position spaces are made adaptive to changes according to the Gbest and Pbest position spaces. Second, a weighted multiobjective optimization for Faster-RCNN is proposed based on minimum loss, average loss, and gradient of loss to give priority scale. The integer EMPSO with adaptive changes to Gbest and Pbest position space is first tested on nine non-linear standard test functions to validate its performance, the results show performance improvement in finding global minimum compared to EMPSO. This tested algorithm is then applied to optimize Faster-RCNN with the weighted cost function, which uses 1300 container images to train the model and then tested on four videos of moving containers at seaports. The results produce better performances regarding the speed and achieving the optimal solution. This technique causes better minimum losses, average losses, intersection over union, confidence score, precision, and accuracy than the results of the default parameters.http://www.sciencedirect.com/science/article/pii/S2405844023004206OptimizationMixed-integer PSOWeighted cost functionFaster RCNNGantry crane control systemContainer detection |
| spellingShingle | Steven Bandong Yul Yunazwin Nazaruddin Endra Joelianto Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation Heliyon Optimization Mixed-integer PSO Weighted cost function Faster RCNN Gantry crane control system Container detection |
| title | Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation |
| title_full | Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation |
| title_fullStr | Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation |
| title_full_unstemmed | Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation |
| title_short | Faster RCNN mixed-integer optimization with weighted cost function for container detection in port automation |
| title_sort | faster rcnn mixed integer optimization with weighted cost function for container detection in port automation |
| topic | Optimization Mixed-integer PSO Weighted cost function Faster RCNN Gantry crane control system Container detection |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023004206 |
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