Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Shepley, Andrew; Falzon, Greg; Kwan, Paul; Branković, Ljiljana

doi:10.1109/tpami.2023.3273210

Cited by 16 publications

(4 citation statements)

References 68 publications

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“…In this section, we examine the performance of the proposed NMS and compare it with the conventional NMS approaches, i.e., the greedy NMS [29], DIoU NMS [32], Soft NMS [31], Exponential NMS [33], WBF [30], and Confluence NMS [34]. To this end, we evaluate these NMS methods on a dataset of steel surface defects using the well-known object detection models YOLOv7 and EfficientDet.…”

Section: Resultsmentioning

confidence: 99%

“…It was shown in [30] that by ensembling BBoxes with the corresponding confidence score instead of suppressing them, the performance of object detection could be improved. Finally, in [34], Confluence NMS was proposed, which utilizes a normalized Manhattan distance-inspired proximity metric instead of the confidence score for clustering BBoxes.…”

Section: Related Workmentioning

confidence: 99%

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Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects

Kang,

Palakonda,

Kim

et al. 2023

Mathematics

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Quality control in manufacturing equipment relies heavily on the detection of steel surface defects. Recently, there have been an increasing number of efforts in which object detection techniques have been utilized to achieve promising results in the detection of steel surface defects since the defect patterns can be considered objects. To enhance the detection performance in the object detection problem, the non-maximum suppression (NMS) step, which eliminates redundant boxes overlapped with a box having the greatest detection score, is essential. In this work, we propose a novel NMS to improve the detection method of steel surface defects. The proposed NMS approach is composed of three novel techniques: IoU regularization, threshold adjustment, and comparison rule modification to enhance the detection performance. To evaluate the performance of the proposed NMS, we carry out extensive numerical experiments using the YOLOv7 and EfficientDet models on the steel surface defect datasets, NEU-DET and GC10-DET. The experimental results demonstrate that the proposed NMS outperforms the conventional NMS methods in both quantitative and qualitative manners.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects

Kang,

Palakonda,

Kim

et al. 2023

Mathematics

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show abstract

“…Statistics [54] Probability and statistics: employ probabilistic models and statistical methods for feature analysis. Non-maximum suppression: removes duplicate or low-confidence bounding boxes.…”

Section: Mathematical Methods Application In Object Detectionmentioning

confidence: 99%

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Manakitsa,

Maraslidis,

Moysis

et al. 2024

Technologies

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Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.

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“…DPP selects a subset of detections that are representative and diverse, which can be beneficial in crowded scenarios where detections overlap. In [18], they propose an alternative to NMS and IoU-based methods called "Confluence". It aims at resolving the challenges associated with redundant detections in object detection tasks, by selecting the box that is closest to every other box within a given cluster and removing highly confluent neighboring boxes.…”

Section: Elimination Of Multiple Detections For the Same Elementmentioning

confidence: 99%

Joint Object Detection and Re-Identification for 3D Obstacle Multi-Camera Systems

Cortés,

Beltrán,

de la Escalera

et al. 2023

Sensors

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The growing on-board processing capabilities have led to more complex sensor configurations, enabling autonomous car prototypes to expand their operational scope. Nowadays, the joint use of LiDAR data and multiple cameras is almost a standard and poses new challenges for existing multi-modal perception pipelines, such as dealing with contradictory or redundant detections caused by inference on overlapping images. In this paper, we address this last issue in the context of sequential schemes like F-PointNets, where object candidates are obtained in the image space, and the final 3D bounding box is then inferred from point cloud information. To this end, we propose the inclusion of a re-identification branch into the 2D detector, i.e., Faster R-CNN, so that objects seen from adjacent cameras can be handled before the 3D box estimation takes place, removing duplicates and completing the object’s cloud. Extensive experimental evaluations covering both the 2D and 3D domains affirm the effectiveness of the suggested methodology. The findings indicate that our approach outperforms conventional Non-Maximum Suppression (NMS) methods. Particularly, we observed a significant gain of over 5% in terms of accuracy for cars in camera overlap regions. These results highlight the potential of our upgraded detection and re-identification system in practical scenarios for autonomous driving.

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Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Cited by 16 publications

References 68 publications

Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects

Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Joint Object Detection and Re-Identification for 3D Obstacle Multi-Camera Systems

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