Precision cutting tools, as the core components for the construction of national key equipment, have attracted growing attention to their quality. Micro-scale defects on the tool surface, such as cracks and bumps, expand rapidly under high-intensity machining environments. The defects at the damaged parts will be continuously amplified, which affects the performance and safety of end products, also severely restricts the efficiency of product processing and manufacturing. The defects on the surface of cutting tools exhibit much smaller morphological and texture features, and this greatly increases the difficulty of automated detection. Therefore, the development of efficient and accurate micro-scale defect detection technology for tool surfaces has become a key factor in intelligent manufacturing. Although a large number of research methods related to different attention mechanisms have been proposed in the published literature. However, when these methods are directly applied to detect tiny appearance defects on the tool surface, the following two key problems will arise: 1) the problem of difficulty in identifying micro-scale defects. When existing attention mechanisms deal with the morphological diversity of tiny defects on the surface of precision cutting tools, they lack effective coupling and expression of channel features, resulting in the inability to accurately identify key information. 2) The problem of defect texture diversity. When facing the diverse texture features of complex defects on the surface of precision cutting tools, traditional methods fail to make full use of frequency domain information to capture subtle changes in defects, which affects the accuracy of detection.

To address these issues, a precision tool defect detection model is proposed based on adaptive multi-parallel attention (AMPA). The core architecture of AMPA consists of two functionally complementary sub-modules: the star channel attention module (SCAM) and the frequency-spatial collaborative attention module (FSAM). Adopting a parallel processing mechanism, the two modules mine defect features from different dimensions: SCAM focuses on the channel dimension, strengthens the nonlinear coupling between channels through star operations, and captures the key channel responses of micro-scale defects; FSAM integrates the frequency and spatial dimensions, extracts full-frequency domain information using fast Fourier transform (FFT), and fuses spatial structural features to address defect localization under complex textured backgrounds. This parallel design can maximize the preservation of the uniqueness of the two types of features-SCAM and FSAM have low feature mutual information (low redundancy), and the total information entropy is higher than that of serial structures (preventing subsequent modules from overwriting preceding features), laying a foundation for efficient feature fusion. From the aspects of actual processing lists as follows. Initially, a star-channel attention module (SCAM) is constructed, utilizing star pperations to enhance nonlinear coupling between channels, thereby improving the representation ability of key channel features. Secondly, a frequency-spatial attention module (FSAM) is designed to combine frequency domain and spatial information, effectively capturing low-frequency, mid-frequency and high-frequency features as well as spatial structural information in images. Finally, the fusion weights of these two modules are dynamically adjusted through learnable parameters to achieve adaptive feature fusion, enhancing the detection performance.

The dual improvements of the proposed methods demonstrate that while incurring a controlled computational cost, the AMPA module not only enhances detection accuracy under a single threshold, but also strengthens the model’s comprehensive detection capability across multiple scenarios and evaluation criteria, fully embodying its application value in multi-category industrial defect detection. Experimental results demonstrate that integrating the proposed method into mainstream YOLOv11 model can achieve a mAP0.5 of 90.2% on a self-constructed precision tool defect dataset, achieving an improvement of 2.4% compared with the traditional YOLOv11. This result validates the model's effectiveness in detecting micro-scale target defects on tool surfaces. Additionally, on the publicly available NEU steel surface defect dataset, the proposed method can reach a mAP0.5 of 76.1%, surpassing traditional YOLOv11 by 1.3%, demonstrating the model's robust generalization capability. In summary, by fusing channel, frequency-domain and spatial features, the AMPA module not only improves detection accuracy under a single threshold, but also boosts robustness across different IoU criteria, effectively optimizing the detection performance for multi-category steel surface defects and verifying the effectiveness and practicality of the proposed method.

Aiming at the problems of existing attention mechanisms in the detection of micro-defects on the surface of precision cutting tools, an adaptive multi-parallel attention (AMPA) module is proposed in this paper. First, the star-channel attention module (SCAM) and the frequency-spatial attention module (FSAM) are executed in parallel, combined with learnable dynamic weights, to realize the adaptive fusion of channel-dimensional features and frequency-spatial features. To verify the effectiveness of the proposed method, the AMPA module is embedded into mainstream YOLO series models (from YOLOv8 to YOLOv11) respectively, and evaluated on a self-built precision cutting tool dataset, achieving a maximum improvement of 2.7% in detection accuracy. For testing the generalization ability of the algorithm, further experiments are conducted on the NEU steel surface defect dataset, with a maximum accuracy improvement of 1.5% obtained. Experimental results show that the AMPA module not only significantly optimizes the detection performance of small-target defects on precision cutting tools, but also effectively enhances the recognition performance of multi-category complex defects on steel surfaces, demonstrating its wide applicability in enhancing feature representation and improving model performance across different industrial scenarios. Although the proposed method shows effectiveness in the detection of micro-scale single-category tool defects, it still has limitations in complex industrial scenarios: its adaptability to multi-category micro-scale defects needs to be enhanced, its robustness against interferences such as extreme illumination and blurring should be improved, and the real-time performance under lightweight deployment still has room for optimization. Future research will further improve the method by introducing lightweight module designs and other strategies.