## 1 Introduction  
In recent years, the rapid development of advanced technology is inseparable from the support of power electronics technology, the use of commutation technology can efficiently achieve the conversion of electrical energy, the Insulated Gate Bipolar Transistor (IGBT) is a variety of electronic power equipment undertaking the conversion of electrical energy and transmission control of the core components [1; 2; 3; 4], it is also the most failure-prone component in converters [5; 6]. With the rise of new energy industries, the efficiency and lifetime of IGBT have become the key to the global green energy-saving economy [7]. IGBT modules are subjected to relatively repetitive alternating high and low temperature and thermal cycles in actual operation for long periods, which makes it imperative that they have excellent reliability in terms of temperature. However, it has been found that the reliability of IGBT modules in high-reliability high power places is not high [8; 9], and that defects in their solder layer interfaces can not only affect the overall model heat transfer but can even lead to the failure of the entire module. Its failure can lead to the failure of electronic and electrical equipment in minor cases, or it can lead to the overall system being unexpectedly plunged into downtime, resulting in serious economic losses [10; 11; 12]. Therefore, IGBT solder layer defect detection is of great importance to ensure the reliability and stability of IGBT modules. The traditional methods for defect detection in IGBT modules include destructive testing sampling with microscopic and nondestructive testing. For nondestructive testing,a series of detection methods were proposed, such as ultrasonic inspection [14], and X-ray detection using digital radiography (DR) and computed tomography (CT). However, in traditional methods, due to the high cost and low efficiency of manual testing, it is difficult to complete the task of large-scale quality testing. As deep learning-based defect detection relies on images, imaging-friendly X-rays become the nexus for detection using deep learning. Object detection algorithms are divided into three categories: single-stage algorithms, two-stage algorithms, and anchor-free algorithms. The two-stage algorithms generate candidate regions in advance with high precision but low speed such as Faster R-CNN [15], Mask R-CNN [16], and Cascade R-CNN [17], etc. The one-stage algorithms that synchronize the target prediction category with the regression position with high speed and high practicality meet the requirements of industrial real-time quality inspection such as YOLO family series [18,19], SSD [20], RetinaNet [21], etc. The anchor-free algorithms avoid hand-designed anchor frames, such as CenterNet [22], FCOS [23], etc. In recent years, the rapid development of hardware technology has led to the rapid growth of computer computing power, laying the foundation for long development in various fields. [PERSON] et al. [24] proposed the implementation of an unsupervised image classification task using a time-multiplexed spiking convolutional neural network based on VCSELs; [PERSON] et al. [25] proposed AI2O3/TiOX-based resistive random access memory (RRAM) in response to the lack of consideration of errors occurring in the program during transfer in previous studies; [PERSON] et al. [26] proposed a technology-aware training algorithm to address the problem that various non-idealities in the crossbar implementations of synaptic arrays can degrade the performance of neural networks.The above hardware development cases offer the possibility and feasibility of deep learning to implement industrial defect detection. With the rapid development of computer computing power, deep learning has contributed to defect detection in industries such as printed circuit boards, steel, capsules, batteries, textiles, fruits and vegetables, and smart cars. For example, [PERSON] et al.