Interactive Incremental Defect Detection Framework With Macroprobability-Controlled Adaptive Learning

Visual surface defect detection plays a crucial role in industrial quality control. A series of deep learning based algorithms have been introduced into visual defect detection, achieving remarkable performance. However, these algorithms generally suffer from inadequate adaptability to the expanding of detection categories, which limits their detection capabilities when dealing with complex and dynamic real-world application scenarios. To address this issue, this paper proposes an innovative incremental defect detection model. This model is based on learnable feature fusion and dynamic category modeling, aiming to enhance the model’s online learning and adaptability to new defect categories. To effectively utilize the existing data and optimize the model training process, this study introduces a macrodata probability control strategy to guide the reasonable configuration of training data as well as the model. Furthermore, to improve the performance during the detection process, an interactive feedback mechanism is constructed. This mechanism provides effective data in real-time during the detection process, driving the model to undergo dynamic training and gradually enhancing its recognition and adaptability to newly emerging defect types. To verify the practicality and effectiveness of the proposed algorithm, comprehensive comparative experiments were conducted on multiple datasets and a real-world detection platform was established for validation. The experimental results demonstrate the superior performance and dynamic adaptability in practical applications.