## 1 Introduction  
With the continuous advancement of manufacturing technology and the increasing complexity of industrial production, the quality requirements for machining mechanical parts have become increasingly stringent. The timely detection of tiny defects on machining surfaces is crucial for ensuring the performance and reliability of components. Traditional methods for detecting defects on steel surfaces rely on visual inspection [1], which poses numerous challenges when dealing with tiny defects, such as low detection efficiency, high miss rates, and difficulty in recognizing complex defects. Therefore, researching and applying advanced detection technologies has become one of the urgent needs to improve component quality and reduce production costs.  
In recent years, the development of deep learning-based image recognition algorithms has provided a new perspective for the detection of tiny defects on the machining surfaces of mechanical parts [2]. Deep learning technology has achieved significant achievements in image classification and object recognition, providing a powerful tool for the efficient detection of tiny defects on machining surfaces.  
[PERSON] [3] et al. proposed a multi-scale defect detection model based on YOLOv5, replacing the neck network with a Slim-Neck to enhance the fusion capabilities of multi-scaledefect features on metal surfaces. [PERSON] [4] and others proposed a Random Crop Augmentation (RCA) method based on an optimized YOLOv8 algorithm, introducing a tiny-object detection layer, which significantly improved the accuracy of detecting defects on the inner walls of cylindrical high-precision part tiny-holes. [PERSON] [5] et al. presented a machine vision solution for detecting defects on precision metal workpiece surfaces, reducing feature redundancy and enhancing salient feature learning by integrating an ELAN-SC module, while introducing a Multi-scale Cross Fusion (MCF) attention module to aggregate local context in weak defect areas. [PERSON] [6] addressed the issue of defect misses by augmenting the training samples through horizontal flipping and using the ResNet50 structure for feature extraction, effectively increasing the defect detection rate. [PERSON] et al. [7] optimized the Faster R-CNN network, replacing the original VGG-16 structure with ResNet, and combined deformable convolutions with a dense structure for feature extraction. This improved method more effectively captures semantic information and has been tested on a custom-built steel surface defect dataset, with results showing that the improved model increased the average precision by 4.3%.  
The existing literature has made certain progress in the field of defect detection, but there are still many deficiencies. Firstly, current studies lack a clear definition of defect size, leading to imprecise performance when dealing with small-sized defects. Secondly, there is still room for improvement in feature classification and target localization. The network designed in this study achieves rapid data classification through an efficient classification decoder and precise defect localization through a localization decoder, significantly enhancing detection performance. Moreover, for high-resolution image processing, this study introduces a real-time sliding window method, segmenting images for detection and classification, transforming most of the target detection tasks into image classification problems, thereby effectively enhancing detection efficiency and localization accuracy. In response to these issues, this study proposes an innovative solution, offering new approaches for complex defect detection tasks.