Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3578 2019 2019 PhD Scholar, Department of Information Technology, Vignan's Foundation for Science, Technology & Research University, Guntur, Andhra Pradesh, India Hima Hima Associate Professor, Department of Information Technology, Vignan's Foundation for Science, Technology & Research University, Guntur, Andhra Pradesh, India Hemanta Kumar Bhuyan Department of Information Technology, VR Siddhartha Engineering College (A), Siddhartha Academy of Higher Education (Deemed to be University), Vijayawada, India; Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), Vishakhapatnam, India E. Laxmi Lydia Pedestrian detection using object detection and deep learning has been found to be effective method for identifying pedestrians in video frames or images accurately. It is more commonly used in many real-time applications, such as security observing systems, autonomous driving systems, and robotics. The combination of deep learning techniques and object detection algorithms allows efficient and robust detection of pedestrians in several real-time scenarios. However, it is necessary to improve the detection efficacy for complex environments such as cases with worse visibility due to weather or daytime, crowd scenes, and rare pose samples. Continuous improvement and research in DL algorithms, dataset collection, and TRA models contribute to accelerating the robustness and acc of pedestrian detection systems. Therefore, this research models a novel marine predator algorithm with DL-based pedestrian detection and classification (MPADLB-PDC) method. The objective of the MPADLB-PDC system lies in the accurate recognition and identification of pedestrians. To achieve this, the MPADLB-PDC technique involves two major processes, namely object detection and classification. In the first stage, the MPADLB-PDC technique uses an improved YOLOv7 object detector for the recognition of the objects in the frame. Next, in the second stage, the ensemble classifier comprises three classifiers such as deep feed-forward neural networks (DFFNNs), extreme learning machine (ELM), and long short-term memory (LSTM). To improve the recognition performance of the ensemble classifier, the MPA is used to optimally select the parameters related to it. The simulation outcome of the MPADLB-PDC technique was authorized on the pedestrian database, and the outcome can be studied in terms of various aspects. The experimentation values validated the better outcome of the MPADLB-PDC approach compared to other approaches. 2025 2025 28 40 10.54216/JISIoT.160103 https://www.americaspg.com/articleinfo/18/show/3578