{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:53:01Z","timestamp":1760953981173,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006520","name":"Faculty of Electrical and Computer Engineering, Cracow University of Technology","doi-asserted-by":"publisher","award":["E-1\/2023"],"award-info":[{"award-number":["E-1\/2023"]}],"id":[{"id":"10.13039\/501100006520","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Target detection in high-contrast, multi-object images and movies is challenging. This difficulty results from different areas and objects\/people having varying pixel distributions, contrast, and intensity properties. This work introduces a new region-focused feature detection (RFD) method to tackle this problem and improve target detection accuracy. The RFD method divides the input image into several smaller ones so that as much of the image as possible is processed. Each of these zones has its own contrast and intensity attributes computed. Deep recurrent learning is then used to iteratively extract these features using a similarity measure from training inputs corresponding to various regions. The target can be located by combining features from many locations that overlap. The recognized target is compared to the inputs used during training, with the help of contrast and intensity attributes, to increase accuracy. The feature distribution across regions is also used for repeated training of the learning paradigm. This method efficiently lowers false rates during region selection and pattern matching with numerous extraction instances. Therefore, the suggested method provides greater accuracy by singling out distinct regions and filtering out misleading rate-generating features. The accuracy, similarity index, false rate, extraction ratio, processing time, and others are used to assess the effectiveness of the proposed approach. The proposed RFD improves the similarity index by 10.69%, extraction ratio by 9.04%, and precision by 13.27%. The false rate and processing time are reduced by 7.78% and 9.19%, respectively.<\/jats:p>","DOI":"10.3390\/s23177556","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T11:45:51Z","timestamp":1693482351000},"page":"7556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Deep Recurrent Learning-Based Region-Focused Feature Detection for Enhanced Target Detection in Multi-Object Media"],"prefix":"10.3390","volume":"23","author":[{"given":"Jinming","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information Science & Technology, Zhejiang Shuren University, Hangzhou 310015, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3804-4925","authenticated-orcid":false,"given":"Ahmed","family":"Alshahir","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7383-6798","authenticated-orcid":false,"given":"Ghulam","family":"Abbas","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southeast University, Nanjing 210096, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0625-6245","authenticated-orcid":false,"given":"Khaled","family":"Kaaniche","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia"}]},{"given":"Mohammed","family":"Albekairi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia"}]},{"given":"Shahr","family":"Alshahr","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia"}]},{"given":"Waleed","family":"Aljarallah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia"}]},{"given":"Anis","family":"Sahbani","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems and Robotics (ISIR), CNRS, Sorbonne University, 75006 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3086-0947","authenticated-orcid":false,"given":"Grzegorz","family":"Nowakowski","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8229-0598","authenticated-orcid":false,"given":"Marek","family":"Sieja","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","first-page":"535","article-title":"Automated overheated region object detection of imagevoltaic module with thermography image","volume":"11","author":"Su","year":"2021","journal-title":"IEEE J. Imagevoltaics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/LGRS.2020.2975541","article-title":"Cross-scale feature fusion for object detection in optical remote sensing images","volume":"18","author":"Cheng","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neucom.2021.05.013","article-title":"A cross-modal edge-guided salient object detection for RGB-D image","volume":"454","author":"Liu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19938","DOI":"10.1038\/s41598-021-97610-y","article-title":"Integrating object detection and image segmentation for detecting the tool wear area on stitched image","volume":"11","author":"Lin","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42467-021-00012-z","article-title":"Object detection for automotive radar point clouds\u2014A comparison","volume":"3","author":"Scheiner","year":"2021","journal-title":"AI Perspect."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"87340","DOI":"10.1109\/ACCESS.2021.3086499","article-title":"Closed-Loop Region of Interest Enabling High Spatial and Temporal Resolutions in Object Detection and Tracking via Wireless Camera","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/JMASS.2020.3025970","article-title":"Multiclass Object Detection in UAV Images Based on Rotation Region Network","volume":"1","author":"Xiao","year":"2020","journal-title":"IEEE J. Miniaturization Air Space Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3616","DOI":"10.1109\/JSEN.2022.3142024","article-title":"Region of Interest Constrained Negative Obstacle Detection and Tracking with a Stereo Camera","volume":"22","author":"Sun","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108437","DOI":"10.1016\/j.patcog.2021.108437","article-title":"Adaptive region-aware feature enhancement for object detection","volume":"124","author":"Fan","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neucom.2021.03.094","article-title":"ERBANet: Enhancing region and boundary awareness for salient object detection","volume":"448","author":"Yao","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.1007\/s11227-018-2569-1","article-title":"Concise feature pyramid region proposal network for multi-scale object detection","volume":"76","author":"Fang","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1007\/s10489-021-02335-0","article-title":"Spatial hierarchy perception and hard samples metric learning for high-resolution remote sensing image object detection","volume":"52","author":"Zhu","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"14547","DOI":"10.1007\/s10489-022-04243-3","article-title":"Few-shot object detection with dense-global feature interaction and dual-contrastive learning","volume":"53","author":"Huang","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104484","DOI":"10.1016\/j.engappai.2021.104484","article-title":"Towards dense people detection with deep learning and depth images","volume":"106","author":"Pizarro","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.neucom.2021.02.073","article-title":"ReinforceNet: A reinforcement learning embedded object detection framework with region selection network","volume":"443","author":"Zhou","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1109\/TCSVT.2019.2900709","article-title":"Bbc net: Bounding-box critic network for occlusion-robust object detection","volume":"30","author":"Kim","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","first-page":"8004905","article-title":"CDD-Net: A context-driven detection network for multiclass object detection","volume":"19","author":"Wu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6917","DOI":"10.1109\/TIP.2021.3099733","article-title":"HCE: Hierarchical context embedding for region-based object detection","volume":"30","author":"Chen","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"184934","DOI":"10.1109\/ACCESS.2020.3027044","article-title":"Patch-Based Three-Stage Aggregation Network for Object Detection in High Resolution Remote Sensing Images","volume":"8","author":"Sui","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"51710","DOI":"10.1109\/ACCESS.2021.3070379","article-title":"Roifusion: 3d object detection from lidar and vision","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103197","DOI":"10.1016\/j.micron.2021.103197","article-title":"Adaptive AFM imaging based on object detection using compressive sensing","volume":"154","author":"Han","year":"2022","journal-title":"Micron"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108312","DOI":"10.1016\/j.compeleceng.2022.108312","article-title":"Cascaded multi-3D-view fusion for 3D-oriented object detection","volume":"103","author":"Sun","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103775","DOI":"10.1016\/j.dsp.2022.103775","article-title":"EFGNet: Encoder steered multi-modality feature guidance network for RGB-D salient object detection","volume":"131","author":"Xia","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"169599","DOI":"10.1016\/j.ijleo.2022.169599","article-title":"MFDetection: A highly generalized object detection network unified with multilevel heterogeneous image fusion","volume":"266","author":"Peng","year":"2022","journal-title":"Optik"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109938","DOI":"10.1016\/j.knosys.2022.109938","article-title":"Salient object detection in low-light images via functional optimization-inspired feature polishing","volume":"257","author":"Yue","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1016\/j.neucom.2022.06.030","article-title":"Two-stage 3D object detection guided by position encoding","volume":"501","author":"Xu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"28841","DOI":"10.1007\/s11042-020-09503-3","article-title":"C-FCN: Corners-based fully convolutional network for visual object detection","volume":"79","author":"Jiao","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4299","DOI":"10.1007\/s00521-020-05255-1","article-title":"Joint deep separable convolution network and border regression reinforcement for object detection","volume":"33","author":"Quan","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-021-01257-8","article-title":"Object detection by crossing relational reasoning based on graph neural network","volume":"33","author":"You","year":"2022","journal-title":"Mach. Vis. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.procs.2022.08.084","article-title":"Solar Panel Hotspot Localization and Fault Classification Using Deep Learning Approach","volume":"204","author":"Pathak","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15959","DOI":"10.1007\/s11042-021-10568-x","article-title":"Salient object detection via cross diffusion-based compactness on multiple graphs","volume":"80","author":"Wang","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.icte.2021.12.016","article-title":"A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection","volume":"9","author":"Choi","year":"2023","journal-title":"ICT Express"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1007\/s10044-021-01009-4","article-title":"Vehicle object detection method based on candidate region aggregation","volume":"24","author":"Zhang","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100126","DOI":"10.1016\/j.atech.2022.100126","article-title":"Performance evaluation of deep learning object detectors for weed detection for cotton","volume":"3","author":"Rahman","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1080\/01691864.2022.2119888","article-title":"Platooning control of drones with real-time deep learning object detection","volume":"37","author":"Dai","year":"2023","journal-title":"Adv. Robot."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pierdicca, R., Paolanti, M., Felicetti, A., Piccinini, F., and Zingaretti, P. (2020). Automatic Faults Detection of Photovoltaic Farms: SolAIr, a Deep Learning-Based System for Thermal Images. Energies, 13.","DOI":"10.3390\/en13246496"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Adeli, H., Ahn, S., and Zelinsky, G.J. (2022). A brain-inspired object-based attention network for multi-object recognition and visual reasoning. biorXiv.","DOI":"10.1101\/2022.04.02.486850"},{"key":"ref_38","unstructured":"(2023, June 29). Open Images 2019\u2014Object Detection. Available online: https:\/\/www.kaggle.com\/c\/open-images-2019-object-detection."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7556\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:43:48Z","timestamp":1760129028000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,31]]},"references-count":38,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177556"],"URL":"https:\/\/doi.org\/10.3390\/s23177556","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,8,31]]}}}