ORB removal mis-matching method based on Mask R-CNN

Bo, 张博 Zhang; Guang-liang, 韩广良 Han

doi:10.3788/yjyxs20183308.0690

Cited by 3 publications

(1 citation statement)

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“…To improve the accuracy of oriented FAST and rotated BRIEF (ORB) matching of multi-object images, Bo et al proposed a method of image ORB mismatch removal based on Mask R-CNN. 31 Firstly, the image is recognized by Faster R-CNN method, and the region of interest and category label marked by a rectangle frame is obtained by regional recommendation network. In this step, the predicted category and coordinate information of the region of interest can be obtained, and the pixel-level correction can be carried out by convolution layer of full convolution network to get the category of the pixel-level object, and then the object segmentation is carried out.…”

Section: Related Workmentioning

confidence: 99%

Research on abnormal object detection in specific region based on Mask R-CNN

Xiong

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: Abnormal Object Detection in Specific Region. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Research on abnormal object detection in specific region based on Mask R-CNN

Xiong

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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Research on Recognition and Localization of Food for Automatic Grasping Operation

Lifu

Cheng

Tiedan

2022

2022 34th Chinese Control and Decision Conference (CCDC)

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Machine Learning-Enhanced ORB Matching Using EfficientPS for Error Reduction

Li,

Su,

Jiang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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The study comes up with a new way to improve the accuracy of image matching in binocular vision systems, especially those that are mounted on vehicles. It combines machine learning with the ORB (Oriented FAST and Rotated BRIEF) image-matching algorithms. Standard ORB matching frequently encounters mismatches in complex and repetitive environments. To minimize false positives in matches, our strategy utilizes the EfficientPS (Efficient Panoptic Segmentations) algorithm, a panoramic segmentation technique that uses machine learning in conjunction with ORB. The procedure begins with the EfficientPS approach, which delivers fine-grained and efficient segmentation of images, assigning semantic category labels and unique identifiers to each pixel. The ORB feature point matching process is refined using semantic data to filter out mismatches between foreground objects and the background effectively. This machine-learning-augmented method significantly decreases the frequency of erroneous matches in intricate settings. Empirical findings from the KITTI dataset demonstrate that in non-targeted environments, the accuracy of our proposed method (0.978) is marginally less than that of LoFTR (0.983). Still, it surpasses other methods when utilizing 50 ORB parameters. In more intricate situations, such as multi-target scenarios with an increased number of ORB parameters (200), our method maintains a high level of accuracy (0.883), outperforming the conventional ORB (0.732) and rivaling the performance of DL-BDLMR (0.790) and ORB-MFD-FPMC (0.835). Our method’s processing time is competitive and slightly higher than the standard ORB, but it improves accuracy. In scenarios without targets and with single targets, our method’s processing time (0.195 seconds and 0.211 seconds, respectively) is greater than that of ORB and ORB-MFD-FPMC. Yet, it is significantly lower than that of LoFTR. In multi-target scenarios, our method’s processing time (0.226 seconds) is considerably better than LoFTR’s (0.445 seconds), effectively balancing processing speed and accuracy. We highlight the efficacy of incorporating machine learning to enhance the robustness of image-matching algorithms in dynamic and complex environments.

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ORB removal mis-matching method based on Mask R-CNN

Cited by 3 publications

References 0 publications

Research on abnormal object detection in specific region based on Mask R-CNN

Research on abnormal object detection in specific region based on Mask R-CNN

Research on Recognition and Localization of Food for Automatic Grasping Operation

Machine Learning-Enhanced ORB Matching Using EfficientPS for Error Reduction

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