Fusion-based modeling of an intelligent algorithm for enhanced object detection using a Deep Learning Approach on radar and camera data

Object detection, the process of detecting and classifying objects within a given environment, forms the foundational element. Multisensory fusion incorporates data from diverse sensors, like radar and cameras, to refine the reliability and accuracy of detection. Further, Radar and camera data fusion refine this process by integrating the unique strength of both technologies, which leverage the radar's proficiency in adverse weather conditions and the camera's high-resolution imaging. This incorporation enhances the object detection systems, which enables them to effectively operate across the spectrum of scenarios, from autonomous vehicles navigating challenging weather to surveillance systems monitoring critical infrastructure. Deep learning (DL), a branch of machine learning (ML), empowers this system with the capability to learn complex representations and patterns directly from the data, which enables them to generalize and adapt to new situations. By integrating the advanced methodology, we can develop strong perception system capable of interpreting and detecting objects accurately in dynamic and diverse environments, from autonomous vehicles navigating urban landscapes to surveillance systems monitoring complex environments. This study designs an Intelligent Algorithm for Enhanced Object Detection Using Deep Learning Approach on the Radar and Camera Data Fusion (IAEODDLRCDF) technique. The presented IAEOD-DLRCDF technique uses multi-angle joint calibration where the spatial sparse alignment of the heterogeneous data of the camera and Radar is realized with image falsification disregarded. Besides, the IAEOD-DLRCDF technique applies YOLOv8 object detector for radar and camera target detection individually which are then integrated with the image plane. Moreover, the detected objects are then classified via the bidirectional long short-term memory (BiLSTM) model. Furthermore, the Adam optimizer is used for the optimum hyperparameter selection of the BiLSTM network which results in a better recognition rate. The performance assessment of the IAEOD-DLRCDF method is tested under benchmark dataset. The empirical analysis stated that the IAEOD-DLRCDF method gains better performance over other models.