Object detection for advanced driving assistance system using a novel machine learning approach: rat swarm Henry gas solubility trained deep quantum network

Due to the unique technologies of intellectual transportation model, the intelligent vehicle becomes the carrier for inclusive amalgamation of several technologies. Even though, vision-assisted automated driving has acquired effective prospects, there becomes an issue to evaluate the complex traffic situations due to accumulated data. Thus, a deep model driven hybridised optimisation is needed for detecting multi-objects. The input video frame first undergoes pre-processing using a Laplace filter to eliminate noise. The pre-processed images are fed to object segmentation using fuzzy local information C-means (FLICM). The segmented images are allowed for feature extraction. For multi-object detection, the extracted features are processed by the deep quantum neural network (DQNN), which is optimised through the rat swarm Henry gas solubility optimisation (RSHGSO) technique. The RSHGSO is the incorporation of rat swarm optimisation (RSO) and Henry gas solubility optimisation (HGSO). RSHGSO-DQNN has significantly lower processing time compared to baseline model. The HGSO-DQNN demonstrated enhanced performance, achieving a remarkable accuracy of 90.9%, an F-measure of 94.7%, and a precision of 92.6%.