{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:29:05Z","timestamp":1763990945663,"version":"3.45.0"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Real-world threat detection systems face critical challenges in adapting to evolving operational conditions while providing transparent decision making. Traditional deep learning models suffer from catastrophic forgetting during continual learning and lack interpretability in security-critical deployments. This study proposes a distributed edge\u2013cloud framework integrating YOLOv8 object detection with incremental learning and Gradient-weighted Class Activation Mapping (Grad-CAM) for adaptive, interpretable threat detection. The framework employs distributed edge agents for inference on unlabeled surveillance data, with a central server validating detections through class verification and localization quality assessment (IoU \u2265 0.5). A lightweight YOLOv8-nano model (3.2 M parameters) was incrementally trained over five rounds using sequential fine tuning with weight inheritance, progressively incorporating verified samples from an unlabeled pool. Experiments on a 5064 image weapon detection dataset (pistol and knife classes) demonstrated substantial improvements: F1-score increased from 0.45 to 0.83, mAP@0.5 improved from 0.518 to 0.886 and minority class F1-score rose 196% without explicit resampling. Incremental learning achieved a 74% training time reduction compared to one-shot training while maintaining competitive accuracy. Grad-CAM analysis revealed progressive attention refinement quantified through the proposed Heatmap Focus Score, reaching 92.5% and exceeding one-shot-trained models. The framework provides a scalable, memory-efficient solution for continual threat detection with superior interpretability in dynamic security environments. The integration of Grad-CAM visualizations with detection outputs enables operator accountability by establishing auditable decision records in deployed systems.<\/jats:p>","DOI":"10.3390\/computers14120511","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:09:25Z","timestamp":1763989765000},"page":"511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image-Based Threat Detection and Explainability Investigation Using Incremental Learning and Grad-CAM with YOLOv8"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7176-6028","authenticated-orcid":false,"given":"Zeynel","family":"Kutlu","sequence":"first","affiliation":[{"name":"Department of Defense Technologies, Sivas University of Science and Technology, 58000 Sivas, Turkey"}]},{"given":"B\u00fclent G\u00fcrsel","family":"Emiro\u011flu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, M\u00fchendislik ve Do\u011fa Bilimleri Fak\u00fcltesi, K\u0131r\u0131kkale University, 71450 K\u0131r\u0131kkale, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shieh, J.L., Haq, Q.M.u., Haq, M.A., Karam, S., Chondro, P., Gao, D.Q., and Ruan, S.J. (2020). Continual learning strategy in one-stage object detection framework based on experience replay for autonomous driving vehicle. Sensors, 20.","DOI":"10.3390\/s20236777"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ashar, A.A.K., Abrar, A., and Liu, J. (2024, January 24\u201326). A survey on object detection and recognition for blurred and low-quality images: Handling, deblurring, and reconstruction. Proceedings of the 2024 8th International Conference on Information System and Data Mining, Los Angeles, CA, USA.","DOI":"10.1145\/3686397.3686413"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_5","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rodriguez-Rodriguez, J.A., L\u00f3pez-Rubio, E., \u00c1ngel-Ruiz, J.A., and Molina-Cabello, M.A. (2024). The impact of noise and brightness on object detection methods. Sensors, 24.","DOI":"10.3390\/s24030821"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101805","DOI":"10.1016\/j.inffus.2023.101805","article-title":"Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence","volume":"99","author":"Ali","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_8","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_12","first-page":"1","article-title":"Evaluating the robustness of YOLO object detection algorithm in terms of detecting objects in noisy environment","volume":"054","year":"2023","journal-title":"J. Sci. Rep.-A"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"122807","DOI":"10.1016\/j.eswa.2023.122807","article-title":"A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations","volume":"242","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1007\/s10994-023-06353-6","article-title":"A survey on learning from imbalanced data streams: Taxonomy, challenges, empirical study, and reproducible experimental framework","volume":"113","author":"Aguiar","year":"2024","journal-title":"Mach. Learn."},{"key":"ref_15","unstructured":"Joshi, P. (2025, November 18). Weapon Detection Computer Vision Model. Available online: https:\/\/universe.roboflow.com\/parthav-joshi\/weapon_detection-xbxnv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual Lifelong Learning with Neural Networks: A Review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s10846-022-01603-6","article-title":"Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks","volume":"105","author":"Shaheen","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., and Lampert, C.H. (2017, January 21\u201326). iCaRL: Incremental Classifier and Representation Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref_19","unstructured":"Kemker, R., and Kanan, C. (May, January 30). FearNet: Brain-Inspired Model for Incremental Learning. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Feng, T., Wang, M., and Yuan, H. (2022, January 18\u201324). Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00921"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110493","DOI":"10.1016\/j.patcog.2024.110493","article-title":"Incremental Convolutional Transformer for Baggage Threat Detection","volume":"153","author":"Hassan","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_22","first-page":"11816","article-title":"Gradient based sample selection for online continual learning","volume":"Volume 32","author":"Aljundi","year":"2019","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS 2019)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1109\/TPAMI.2018.2832629","article-title":"Imbalanced Deep Learning by Minority Class Incremental Rectification","volume":"41","author":"Dong","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Doshi-Velez, F., and Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. arXiv."},{"key":"ref_25","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. (2018, January 3\u20138). Sanity Checks for Saliency Maps. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS 2018), Montr\u00e9al, QC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107197","DOI":"10.1016\/j.infsof.2023.107197","article-title":"Transparency and Explainability of AI Systems: From Ethical Guidelines to Requirements","volume":"159","author":"Balasubramaniam","year":"2023","journal-title":"Inf. Softw. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI\u2014Explainable Artificial Intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bhati, D., Neha, F., and Amiruzzaman, M. (2024). A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging. J. Imaging, 10.","DOI":"10.20944\/preprints202408.0765.v1"},{"key":"ref_29","unstructured":"Ultralytics (2025, November 18). Hyperparameter Tuning Guide\u2014YOLOv8. Available online: https:\/\/docs.ultralytics.com\/guides\/hyperparameter-tuning\/."},{"key":"ref_30","unstructured":"Loshchilov, I., and Hutter, F. (2019). Decoupled Weight Decay Regularization. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mulajkar, R., and Yede, S. (2024, January 24\u201326). YOLO Version v1 to v8 Comprehensive Review. Proceedings of the 2024 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal.","DOI":"10.1109\/ICICT60155.2024.10544452"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ali, M.L., and Zhang, Z. (2024). The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection. Computers, 13.","DOI":"10.20944\/preprints202410.1785.v1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"192747","DOI":"10.1109\/ACCESS.2025.3630988","article-title":"A Decade of You Only Look Once (YOLO) for Object Detection: A Review","volume":"13","author":"Ramos","year":"2025","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hussain, M. (2023). YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines, 11.","DOI":"10.3390\/machines11070677"},{"key":"ref_35","first-page":"37","article-title":"Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. Mach. Learn. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., and Doll\u00e1r, P. (2014). Microsoft COCO: Common Objects in Context. arXiv.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Papadopoulos, D.P., Uijlings, J.R.R., Keller, F., and Ferrari, V. (2017, January 22\u201329). Extreme Clicking for Efficient Object Annotation. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.528"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., and McGuinness, K. (2020, January 19\u201324). Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref_39","unstructured":"Richardson, L., and Ruby, S. (2007). RESTful Web Services, O\u2019Reilly Media, Inc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MIC.2012.64","article-title":"Communicating and Displaying Real-Time Data with WebSocket","volume":"16","author":"Pimentel","year":"2012","journal-title":"IEEE Internet Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.33411\/ijist\/20257212691280","article-title":"Deep Learning-based Weapon Detection using Yolov8","volume":"7","author":"Farhan","year":"2025","journal-title":"Int. J. Innov. Sci. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"26766","DOI":"10.1038\/s41598-025-07782-0","article-title":"Weapon detection with FMR-CNN and YOLOv8 for enhanced crime prevention and security","volume":"15","author":"Shanthi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1613\/jair.1.12125","article-title":"Confident Learning: Estimating Uncertainty in Dataset Labels","volume":"70","author":"Northcutt","year":"2021","journal-title":"J. Artif. Intell. Res."},{"key":"ref_44","first-page":"8527","article-title":"Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels","volume":"Volume 31","author":"Han","year":"2018","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NeurIPS 2018)"},{"key":"ref_45","unstructured":"Sapkota, R., and Karkee, M. (2025). Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence","volume":"16","author":"Hassija","year":"2024","journal-title":"Cogn. Comput."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/12\/511\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:26:54Z","timestamp":1763990814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/12\/511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,24]]},"references-count":46,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["computers14120511"],"URL":"https:\/\/doi.org\/10.3390\/computers14120511","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,24]]}}}