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Decoupled Sequential Detection Head for accurate acne detection
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AI
DOI：10.1016/j.knosys.2023.111305.png)
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摘要
En 中文
Accurate detection of acne plays a crucial role in acquiring precise diagnosis and conducting proper therapy. However, the ambiguous boundaries and arbitrary dimensions of acne lesions severely limit the performance of existing methods. In this study, we address these challenges via a novel Decoupled Sequential Detection Head (DSDH), which can be easily adopted by mainstream two-stage detectors. DSDH brings two simple effective improvements to acne detection. Firstly, the offset and scaling tasks are explicitly introduced, and their incompatibility is settled by our task-decouple mechanism, which improves the capability of predicting the location and size of acne lesions. Second, we propose the task-sequence mechanism, and execute offset and scaling sequentially to gain a more comprehensive insight into the dimensions of acne lesions. In addition, build a high-quality acne detection dataset named ACNE-DET to verify the effectiveness of DSDH. Experiments on ACNE-DET and the public benchmark ACNE04 show that our method outperforms the previous sophisticated methods by significant margins. Our DSDH and ACNE-DET are pioneering works in acne detection, establishing the state-of-the-art architecture and benchmark, respectively.
Keyword:
Object detection
Detection head
Bounding box
Acne vulgaris
Decouple method
Sequence method
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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