CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA
Researchers are studying CNN (convolutional neural networks) in various ways for image classification. Sometimes, they must classify two or more objects in an image into different situations according to their location. We developed a new learning method that colored objects from images and extracte...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4291 |
| _version_ | 1797608340859125760 |
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| author | Sac Lee Jaemin Hwang Junbeom Kim Jinho Han |
| author_facet | Sac Lee Jaemin Hwang Junbeom Kim Jinho Han |
| author_sort | Sac Lee |
| collection | DOAJ |
| description | Researchers are studying CNN (convolutional neural networks) in various ways for image classification. Sometimes, they must classify two or more objects in an image into different situations according to their location. We developed a new learning method that colored objects from images and extracted them to distinguish the relationship between objects with different colors. We can apply this method in certain situations, such as pedestrians in a crosswalk. This paper presents a method for learning pedestrian situations on CNN using Mask R-CNN (Region-based CNN) and CDA (Crosswalk Detection Algorithm). With this method, we classified the location of the pedestrians into two situations: safety and danger. We organized the process of preprocessing and learning images into three stages. In Stage 1, we used Mask R-CNN to detect pedestrians. In Stage 2, we detected crosswalks with the CDA and placed colors on detected objects. In Stage 3, we combined crosswalks and pedestrian objects into one image and then, learned the image to CNN. We trained ResNet50 and Xception using images in the proposed method and evaluated the accuracy of the results. When tested experimentally, ResNet50 exhibited 96.7% accuracy and Xception showed 98.7% accuracy. We then created an image that simplified the situation with two colored boxes of crosswalks and pedestrians. We confirmed that the learned CNN with the images of colored boxes could classify the same test images applied in the previous experiment with 96% accuracy by ResNet50. This result indicates that the proposed system is suitable for classifying pedestrian safety and dangerous situations by accurately dividing the positions of the two objects. |
| first_indexed | 2024-03-11T05:43:05Z |
| format | Article |
| id | doaj.art-99a515bb0d724f23b4adddad51748db5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:43:05Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-99a515bb0d724f23b4adddad51748db52023-11-17T16:18:16ZengMDPI AGApplied Sciences2076-34172023-03-01137429110.3390/app13074291CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDASac Lee0Jaemin Hwang1Junbeom Kim2Jinho Han3Department of Computer Software, Korean Bible University, Seoul 01757, Republic of KoreaDepartment of Computer Software, Korean Bible University, Seoul 01757, Republic of KoreaDepartment of Computer Software, Korean Bible University, Seoul 01757, Republic of KoreaDepartment of Liberal Studies (Computer), Korean Bible University, Seoul 01757, Republic of KoreaResearchers are studying CNN (convolutional neural networks) in various ways for image classification. Sometimes, they must classify two or more objects in an image into different situations according to their location. We developed a new learning method that colored objects from images and extracted them to distinguish the relationship between objects with different colors. We can apply this method in certain situations, such as pedestrians in a crosswalk. This paper presents a method for learning pedestrian situations on CNN using Mask R-CNN (Region-based CNN) and CDA (Crosswalk Detection Algorithm). With this method, we classified the location of the pedestrians into two situations: safety and danger. We organized the process of preprocessing and learning images into three stages. In Stage 1, we used Mask R-CNN to detect pedestrians. In Stage 2, we detected crosswalks with the CDA and placed colors on detected objects. In Stage 3, we combined crosswalks and pedestrian objects into one image and then, learned the image to CNN. We trained ResNet50 and Xception using images in the proposed method and evaluated the accuracy of the results. When tested experimentally, ResNet50 exhibited 96.7% accuracy and Xception showed 98.7% accuracy. We then created an image that simplified the situation with two colored boxes of crosswalks and pedestrians. We confirmed that the learned CNN with the images of colored boxes could classify the same test images applied in the previous experiment with 96% accuracy by ResNet50. This result indicates that the proposed system is suitable for classifying pedestrian safety and dangerous situations by accurately dividing the positions of the two objects.https://www.mdpi.com/2076-3417/13/7/4291computer visionobject detectionMask R-CNNcrosswalkpedestrian |
| spellingShingle | Sac Lee Jaemin Hwang Junbeom Kim Jinho Han CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA Applied Sciences computer vision object detection Mask R-CNN crosswalk pedestrian |
| title | CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA |
| title_full | CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA |
| title_fullStr | CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA |
| title_full_unstemmed | CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA |
| title_short | CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA |
| title_sort | cnn based crosswalk pedestrian situation recognition system using mask r cnn and cda |
| topic | computer vision object detection Mask R-CNN crosswalk pedestrian |
| url | https://www.mdpi.com/2076-3417/13/7/4291 |
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