Research on Intelligent Detection Algorithm of Small Foreign Objects in Images Based on Deep Learning

To address the challenge of identifying small foreign objects in images, this study introduces an advanced algorithm integrating deep learning with multi-scale detection methodologies. The algorithm constructs a sophisticated multilayer convolutional neural network (CNN) architecture, which thoroughly explores the multi-scale attributes of the image. This is then combined with an anomaly detection framework to achieve precise identification of small foreign objects within images. The model leverages the robust feature extraction capabilities of the CNN to capture fine details across various scales, enabling the effective detection and localization of anomalous regions within the image. To evaluate the algorithm's effectiveness and applicability, a series of simulations were conducted across multiple scenarios. The results from these simulations indicate that the algorithm consistently achieves a detection accuracy rate exceeding 95% in diverse environments, while maintaining a false detection rate below 5%, thereby demonstrating the algorithm's stability and efficiency in handling complex backgrounds. Furthermore, the simulation outcomes reveal that the system exhibits strong real-time performance and resilience when applied to intricate industrial settings, further underscoring the algorithm's significant potential for widespread use in industrial inspection and quality control processes.