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Article summary:
1. A new channel-space adaptive enhancement feature pyramid network (CA-FPN) is proposed to eliminate interference from complex backgrounds in defect detection.
2. An anchor-free detector CA-AutoAssign is proposed by combining CA-FPN and an anchor-free detection strategy AutoAssign.
3. Experiments show that CA-AutoAssign has the best detection performance with AP50 reaching 89.1 and 82.7, respectively, while processing time has barely increased.
Article analysis:
The article provides a detailed description of the proposed method for defect detection in complex backgrounds, as well as its evaluation results on two datasets. The authors have provided sufficient evidence to support their claims, such as experimental results and code availability for further verification of their findings. The article also presents both sides of the argument equally, providing a balanced view of the topic at hand. However, there are some potential biases that should be noted in the article, such as the limited scope of datasets used for evaluation and lack of comparison with other methods that could provide additional insights into the effectiveness of the proposed approach. Additionally, more details about how the model was trained and tested would be beneficial to better understand its performance in different scenarios.