Vehicle Detection through Instance Segmentation using Mask R-CNN for Intelligent Vehicle System

Ojha, Apoorva; Sahu, Satya Prakash; Dewangan, Deepak Kumar

doi:10.1109/iciccs51141.2021.9432374

Cited by 36 publications

(9 citation statements)

References 35 publications

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“…When it comes to small numbers of labeled samples, the technique can help reduce the test/validation set error by imposing a regularizing effect. Many studies have illustrated the effectiveness of transferring learning in decreasing the overfitting effect while increasing the prediction accuracy of the Mask R-CNN algorithm in various fields like transportation ( 44,74–76 ), agriculture ( 77 ), and medical ( 78 , 79 ). Therefore, the research also applied this technique on the Rotated Mask R-CNN variants.…”

Section: Methodsmentioning

confidence: 99%

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Truong

Clay

Mora

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Parking space management systems help organize and optimize available parking spaces for consumers, making the process of finding and using parking spaces more efficient. Current parking space management systems include manual recognition, the employment of magnetic and ultrasonic sensors, and, recently, computer vision (CV). One relatively new region-based convolutional neural network (R-CNN) model, Mask R-CNN, has shown promise in its ability to detect objects and has demonstrated superior performance over many other popular CV methods. Building on Mask R-CNN, an updated version, Rotated Mask R-CNN, which can generate bounding boxes the axes of which are rotated with respect to the image’s axis, was proposed to address the limitation of Mask R-CNN. Albeit with the documented theoretical benefits, the application of the rotated version is rare because of its recent invention. To this end, the study aims to detect vehicle instances in one parking lot using various Rotated Mask R-CNN models based on unmanned aircraft system collected images. Both average precision and average recall were utilized to assess the performance of the alternative models with different backbone and head networks. The results reveal the high accuracy level associated with Rotated Mask R-CNN in real-time detection of vehicles. In addition, the results indicate that the inference speed and total loss are highly correlated with head networks and training schedules.

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Section: Methodsmentioning

confidence: 99%

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Truong

Clay

Mora

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…Polygonal annotation of the frontal region of the vehicle is a novel approach leading to a high mAP of 99.67% for segmentation. Thus, when compared to the full vehicle instance segmentation using the KITTI dataset, the model achieved only 92%, as demonstrated in [ 18 ].…”

Section: Introductionmentioning

confidence: 90%

Vehicle Instance Segmentation Polygonal Dataset for a Private Surveillance System

Ottakath

Al-Maadeed

2023

Sensors

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Vehicle identification and re-identification is an essential tool for traffic surveillance. However, with cameras at every corner of the street, there is a requirement for private surveillance. Automated surveillance can be achieved through computer vision tasks such as segmentation of the vehicle, classification of the make and model of the vehicle and license plate detection. To achieve a unique representation of every vehicle on the road with just the region of interest extracted, instance segmentation is applied. With the frontal part of the vehicle segmented for privacy, the vehicle make is identified along with the license plate. To achieve this, a dataset is annotated with a polygonal bounding box of its frontal region and license plate localization. State-of-the-art methods, maskRCNN, is utilized to identify the best performing model. Further, data augmentation using multiple techniques is evaluated for better generalization of the dataset. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches on the same dataset. A classification accuracy of 99.2% was obtained and segmentation was achieved with a high mAP of 99.67%. Data augmentation approaches were employed to balance and generalize the dataset of which the mosaic-tiled approach produced higher accuracy.

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“…Since the target of the method is to reduce the number of computations, the approach uses the YOLOv3 architecture for vehicle detection. Ojha et al [14] proposed an instance-based segmentation-based model for vehicle detection. To achieve this, the method uses R-CNN transfer learning.…”

Section: Related Workmentioning

confidence: 99%

Acmrvd: Augmented Confidence Map Based Roboust Vehicle Detection

Mokayed¹,

Shivakumara

Lama³

et al. 2022

SSRN Journal

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Vehicle Detection through Instance Segmentation using Mask R-CNN for Intelligent Vehicle System

Cited by 36 publications

References 35 publications

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Rotated Mask Region-Based Convolutional Neural Network Detection for Parking Space Management System

Vehicle Instance Segmentation Polygonal Dataset for a Private Surveillance System

Acmrvd: Augmented Confidence Map Based Roboust Vehicle Detection

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