Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
Recent advancements in object detection, particularly with DiffusionDet, have demonstrated impressive performance. However, its reliance on numerous random noise-based object candidates limits its efficiency. To overcome this limitation, we propose DifAda, a novel object detection model that incorpo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/11367 |
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| author | Xingyu Zou Kaixu Han Xinle Zhang Wenhao Wang Ning Wu |
| author_facet | Xingyu Zou Kaixu Han Xinle Zhang Wenhao Wang Ning Wu |
| author_sort | Xingyu Zou |
| collection | DOAJ |
| description | Recent advancements in object detection, particularly with DiffusionDet, have demonstrated impressive performance. However, its reliance on numerous random noise-based object candidates limits its efficiency. To overcome this limitation, we propose DifAda, a novel object detection model that incorporates adaptive noise into the diffusion framework. DifAda employs an adaptive noise mechanism that blends random noise with latent codes, enhancing object candidate utilization and improving object localization. Our model features a simplified decoder structure by employing a single decoder layer and utilizes an adaptive interaction mechanism to further refine feature representations, leading to improved performance with fewer parameters. In addition, DifAda supports flexible speed–accuracy trade-offs through adjustable sampling and iteration steps, requiring no retraining. Experimental results across multiple benchmarks, including COCO and RUOD, demonstrate that DifAda achieves competitive performance with significantly fewer object candidates and parameters. Our findings suggest that DifAda represents a step forward in efficient and scalable object detection. |
| format | Article |
| id | doaj-art-6962d7b24aab46fc98da749167a8348a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6962d7b24aab46fc98da749167a8348a2025-08-20T01:55:41ZengMDPI AGApplied Sciences2076-34172024-12-0114231136710.3390/app142311367Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object DetectionXingyu Zou0Kaixu Han1Xinle Zhang2Wenhao Wang3Ning Wu4School of Computer, Electronics and Information, Guangxi University, Nanning 530004, ChinaGuangxi Key Laboratory of Ocean Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, ChinaCollege of Naval Architecture and Ocean Engineering, Beibu Gulf University, Qinzhou 535011, ChinaCollege of Naval Architecture and Ocean Engineering, Beibu Gulf University, Qinzhou 535011, ChinaCollege of Naval Architecture and Ocean Engineering, Beibu Gulf University, Qinzhou 535011, ChinaRecent advancements in object detection, particularly with DiffusionDet, have demonstrated impressive performance. However, its reliance on numerous random noise-based object candidates limits its efficiency. To overcome this limitation, we propose DifAda, a novel object detection model that incorporates adaptive noise into the diffusion framework. DifAda employs an adaptive noise mechanism that blends random noise with latent codes, enhancing object candidate utilization and improving object localization. Our model features a simplified decoder structure by employing a single decoder layer and utilizes an adaptive interaction mechanism to further refine feature representations, leading to improved performance with fewer parameters. In addition, DifAda supports flexible speed–accuracy trade-offs through adjustable sampling and iteration steps, requiring no retraining. Experimental results across multiple benchmarks, including COCO and RUOD, demonstrate that DifAda achieves competitive performance with significantly fewer object candidates and parameters. Our findings suggest that DifAda represents a step forward in efficient and scalable object detection.https://www.mdpi.com/2076-3417/14/23/11367object detectiondiffusion modelneural network |
| spellingShingle | Xingyu Zou Kaixu Han Xinle Zhang Wenhao Wang Ning Wu Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection Applied Sciences object detection diffusion model neural network |
| title | Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection |
| title_full | Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection |
| title_fullStr | Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection |
| title_full_unstemmed | Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection |
| title_short | Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection |
| title_sort | adaptive noise powered diffusion model for efficient and accurate object detection |
| topic | object detection diffusion model neural network |
| url | https://www.mdpi.com/2076-3417/14/23/11367 |
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