Remote intelligent perception system for multi-object detection

Abdulwahab Alazeb; Bisma Riaz Chughtai; Naif Al Mudawi; Yahya AlQahtani; Mohammed Alonazi; Hanan Aljuaid; Ahmad Jalal; Hui Liu

doi:10.3389/fnbot.2024.1398703

Remote intelligent perception system for multi-object detection

Front Neurorobot. 2024 May 20:18:1398703. doi: 10.3389/fnbot.2024.1398703. eCollection 2024.

Authors

Abdulwahab Alazeb¹, Bisma Riaz Chughtai², Naif Al Mudawi¹, Yahya AlQahtani³, Mohammed Alonazi⁴, Hanan Aljuaid⁵, Ahmad Jalal², Hui Liu⁶

Affiliations

¹ Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, Saudi Arabia.
² Department of Computer Science, Air University, Islamabad, Pakistan.
³ Department of Computer Science, Applied College, King Khalid University, Abha, Saudi Arabia.
⁴ Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁵ Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), Riyadh, Saudi Arabia.
⁶ Cognitive Systems Lab, University of Bremen, Bremen, Germany.

Abstract

Introduction: During the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.

Methods: Advances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.

Results: The performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.

Discussion: Furthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Keywords: AlexNet; deep belief network; deep learning; image processing; intelligent perception; remote sensing; robotic environment.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The APC was funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen. Also, this research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R54), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding program grant code (NU/GP/SERC/13/30). This study is supported via funding from Prince sattam bin Abdulaziz University project number (PSAU/2024/R/1445).