UNet Based on Multi-Object Segmentation and Convolution Neural Network for Object Recognition

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes. Various technologies, such as augmented reality-driven scene integration, robotic navigation, autonomous driving, and guided tour systems, heavily rely on this type of scene comprehension. This paper presents a novel segmentation approach based on the UNet network model, aimed at recognizing multiple objects within an image. The methodology begins with the acquisition and preprocessing of the image, followed by segmentation using the fine-tuned UNet architecture. Afterward, we use an annotation tool to accurately label the segmented regions. Upon labeling, significant features are extracted from these segmented objects, encompassing KAZE (Accelerated Segmentation and Extraction) features, energy-based edge detection, frequency-based, and blob characteristics. For the classification stage, a convolution neural network (CNN) is employed. This comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in images. The experimental results, which include complex object datasets like MSRC-v2 and PASCAL-VOC12, have been documented. After analyzing the experimental results, it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%, while the MSRC-v2 dataset achieved an accuracy of 89%. The evaluation performed on these diverse datasets highlights a notably impressive level of performance.