## 1 Introduction  
Towards smart factory for Industry 4.0, steel strip has become a ubiquitous material in most manufacturing workshops. In reality, owing to external factors such as equipment fatigue, human negligence, and external force, the steel surface may contain various types of defects. Consequently, these surface defects potentially affect the capability of steel products such as wear resistance, fatigue strength, and residual life [1, 2], leading to huge economic losses for manufacturers and posing a high risk to worker safety. As such, defect recognition is an essential task for assuring product quality in manufacturing. Traditionally, defect inspection is performed manually by experienced laborers. However, this inspection task is time-consuming, inefficient, highly subjective, and unreliable under the heavy workload in the high-speed production line. Specifically, inspectors can only cover approximately 0.05% of total steel production [3], and the metal surface defects recognition rate is about 80%, despite most of them being trained professionally [4].  
To overcome the shortcomings of human visual inspection, automated surface inspection (ASI) has drawn extensive attention from the computer vision community and the related research has grown rapidly in recent years. This paper attempts to tackle the surface defect inspection task on hot-rolled steel strips using a lightweight dataset. Succinctly, a two-level Gaussian pyramid with two multi-receptive field networks is introduced. Specifically, the Gaussian pyramid is applied to provide more meaningful samples for training models, at the same time suppressing the background noises from the raw images. Then, two pre-trained GoogLeNets [5] are fine-tuned separately, in which the shallower layers contain higher learning rate factors to improve the convergence speed of the model, while avoiding the training model falling into the local optimal situation. In addition, higher-level model is employed with fewer training parameters owing to the Gaussian pyramid process. Lastly, the prediction scores of both networks will be fused as the final prediction result. To further demonstrate the robustness of the proposed method, several experiments were carried out against Gaussian white noise, salt and pepper noise, and motion blur based on the disturbance defect dataset results. In brief, the main contributions of this work are summarized as follows:  
1. A multi-receptive field fusion-based network with a two-level Gaussian pyramid is introduced to extract more representative information from limited data.
2. The shallower layers of the multi-receptive field fusion-based network (MRFFN) are applied with a higher learning rate to accelerate the convergence of the training process contemporary to avoid training models falling into the local optimal. Moreover, the higher-level model is fine-tuned with fewer training parameters to prevent the overfitting phenomenon, an inherent limitation.
3. The proposed MRFFN achieves a pleasing performance compared with the state-of-the-art, which was trained by a relatively larger dataset. Furthermore, the MRFFN has shown its robustness against disturbance defect datasets, including Gaussian white noise, salt and pepper noise, and motion blur.  
The remainder of the article is organized as follows. Section 2 reviews the related works based on defect recognition. Section 3 elaborates the details of the Gaussian pyramid and the proposed MRFFN. In Section 4, the evaluation of the proposed method will be presented. Section 5 reports and discusses the experimental results. Lastly, Section 6 concludes the article.