Enhancing Object Detection in Dense Images: Adjustable Non-Maximum Suppression for Single-Class Detection
Deep learning-based object detection technology often relies on non-maximum suppression (NMS) algorithms to eliminate redundant detections. However, the conventional NMS algorithm struggles with distinguishing between overlapping and small objects due to its simple constraints. While Soft-NMS offers...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10679138/ |
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| Summary: | Deep learning-based object detection technology often relies on non-maximum suppression (NMS) algorithms to eliminate redundant detections. However, the conventional NMS algorithm struggles with distinguishing between overlapping and small objects due to its simple constraints. While Soft-NMS offers a slight improvement in object detection performance, it still falls short in addressing this challenge. Our proposed solution, adjustable-NMS, represents a significant advancement. While performing comparably to NMS and Soft-NMS on less dense images where objects are easily countable, adjustable-NMS excels in scenarios with higher object density or smaller objects. In such cases, it outperforms both NMS and Soft-NMS, showcasing notably superior object detection capabilities. On average, the improvement achieved with adjustable-NMS reaches an impressive 33.3%. This demonstrates adjustable-NMS’s efficacy in enhancing object detection accuracy, particularly in challenging environments characterized by dense scenes or diminutive objects. |
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| ISSN: | 2169-3536 |