Target image detection algorithm of complex road scene based on improved multi-scale adaptive feature fusion technology

Understanding road scenes is crucial to the safe driving of autonomous vehicles, and object detection in road scenes is necessary to develop driving assistance systems. Current object detection algorithms are not very good at handling complex road scenes, and public datasets do not always adequately represent city traffic. Using Improved Multi-Scale Adaptive Feature Fusion Technology (IMSAFFT), this work suggests a real-time traffic information identification method to fix the issues of low detection accuracy of road scenes and high false detection rates in panoramic video images. In addition, a semantic recognition algorithm for a road scene based on image data is suggested. This study introduces computer vision-based approaches, including colour and texture recognition, object detection, and scene context understanding using Deep Neural Networks (DNN). An increasing number of deeper stacked layers allows the deep neural network to learn more complicated high-level semantic features, and the features' quality improves with time. A learning rate adaptive adjustment technique has been utilized to make training more efficient. After that, this improved detector is used to identify vehicles in original road environments. The suggested technique surpassed traditional detectors in the experiments with a high accuracy rate and processing speed. It worked well in real-world traffic situations for detecting overlapping, multiple, distant, and small objects. The simulation outcomes illustrate that the recommended IMSAFFT model increases the accuracy ratio of 98.4%, target image detection ratio of 97.4%, traffic prediction rate of 96.5%, processing speed rate of 10.4% and F1-score ratio of 95.4% compared to other existing models.