Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
Source abstract
Abstract
Gun violence is a global problem that affects communities and individuals, posing challenges to safety and well-being. The use of autonomous weapons detection systems could significantly improve security worldwide. Despite notable progress in the field of weapons detection closed-circuit television-based systems, several challenges persist, including real-time detection, improved accuracy in detecting small objects, and reducing false positives. This paper, based on our extensive experience in this field and successful private company contracts, presents a detection scheme comprising two modules that enhance the performance of a renowned detector. These modules not only augment the detector’s performance but also have a low negative impact on the inference time. Additionally, a scale-matching technique is utilised to enhance the detection of weapons with a small aspect ratio. The experimental results demonstrate that the scale-matching method enhances the detection of small objects, with an improvement of +13.23 in average precision compared to the non-use of this method. Furthermore, the proposed detection scheme effectively reduces the number of false positives (a 71% reduction in the total number of false positives) of the baseline model, while maintaining a low inference time (34 frames per second on an NVIDIA GeForce RTX-3060 card with a resolution of 720 pixels) in comparison to the baseline model (47 frames per second).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fault Detection and Control Systems · Industrial Vision Systems and Defect Detection · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, 41012 Sevilla, Spain
Funding
project PID2021-126359OB-I00
References (65)
Show source-provided references
1. Real-time gun detection in CCTV: An open problem10.1016/j.neunet.2020.09.013 ↗
2. A motion-based image processing system for detecting potentially dangerous situations in underground railway stations10.1016/j.trc.2006.05.006 ↗
3. Qi, D., Tan, W., Liu, Z., Yao, Q., and Liu, J. (2021, January 17–20). A Dataset and System for Real-Time Gun Detection in Surveillance Video Using Deep Learning. Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia.10.1109/SMC52423.2021.9659207 ↗
4. Gu, Y., Liao, X., and Qin, X. (2022). YouTube-GDD: A challenging gun detection dataset with rich contextual information. arXiv.
5. Qiao, L., Li, X., and Jiang, S. (2016, January 19–21). RGB-D Object Recognition from Hand-Held Object Teaching. Proceedings of the International Conference on Internet Multimedia Computing and Service, ICIMCS’16, Xi’an, China.10.1145/3007669.3007713 ↗
6. Automatic handgun detection alarm in videos using deep learning10.1016/j.neucom.2017.05.012 ↗
7. A binocular image fusion approach for minimizing false positives in handgun detection with deep learning10.1016/j.inffus.2018.11.015 ↗
8. Romero, D., and Salamea, C. (2019). Convolutional models for the detection of firearms in surveillance videos. Appl. Sci., 9.10.3390/app9152965 ↗
9. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
10. Pang, L., Liu, H., Chen, Y., and Miao, J. (2020). Real-time concealed object detection from passive millimeter wave images based on the YOLOv3 algorithm. Sensors, 20.10.3390/s20061678 ↗
11. Fighting against terrorism: A real-time CCTV autonomous weapons detection based on improved YOLO v410.1016/j.dsp.2022.103790 ↗
12. Ahmed, S., Bhatti, M.T., Khan, M.G., Lövström, B., and Shahid, M. (2022). Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos. Appl. Sci., 12.10.3390/app12125772 ↗
13. Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning10.1016/j.neucom.2018.10.076 ↗
14. Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance10.1016/j.knosys.2020.105590 ↗
15. Salido, J., Lomas, V., Ruiz-Santaquiteria, J., and Deniz, O. (2021). Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Appl. Sci., 11.10.3390/app11136085 ↗
16. Weapons detection for security and video surveillance using cnn and YOLO-v5s
17. Goenka, A., and Sitara, K. (2022, January 27–29). Weapon Detection from Surveillance Images using Deep Learning. Proceedings of the 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India.10.1109/INCET54531.2022.9824281 ↗
18. Perea-Trigo, M., López-Ortiz, E.J., Salazar-González, J.L., Álvarez-García, J.A., and Vegas Olmos, J.J. (2022). Data Processing Unit for Energy Saving in Computer Vision: Weapon Detection Use Case. Electronics, 12.10.3390/electronics12010146 ↗
19. Hnoohom, N., Chotivatunyu, P., and Jitpattanakul, A. (2022). ACF: An armed CCTV footage dataset for enhancing weapon detection. Sensors, 22.10.3390/s22197158 ↗
20. A deep-learning framework running on edge devices for handgun and knife detection from indoor video-surveillance cameras10.1007/s11042-023-16231-x ↗
21. Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C.L. (2014, January 6–12). Microsoft coco: Common objects in context. Proceedings of the Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.10.1007/978-3-319-10602-1_48 ↗
22. Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods10.1016/j.neunet.2018.01.005 ↗
23. Evaluation of deep neural networks for traffic sign detection systems10.1016/j.neucom.2018.08.009 ↗
24. Yu, X., Han, Z., Gong, Y., Jan, N., Zhao, J., Ye, Q., Chen, J., Feng, Y., Zhang, B., and Wang, X. (2020, January 23–28). The 1st tiny object detection challenge: Methods and results. Proceedings of the Computer Vision–ECCV 2020 Workshops, Glasgow, UK. Proceedings, Part V 16.10.1007/978-3-030-68238-5_23 ↗
25. Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., and Yan, S. (2017, January 21–26). Perceptual generative adversarial networks for small object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.211 ↗
26. SSPNet: Scale Selection Pyramid Network for Tiny Person Detection From UAV Images10.1109/LGRS.2022.3214929 ↗
27. Yu, X., Gong, Y., Jiang, N., Ye, Q., and Han, Z. (2020, January 1–5). Scale Match for Tiny Person Detection. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.10.1109/WACV45572.2020.9093394 ↗
28. Zhang, W., Wang, S., Thachan, S., Chen, J., and Qian, Y. (2018, January 22–27). Deconv R-CNN for Small Object Detection on Remote Sensing Images. Proceedings of the IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.10.1109/IGARSS.2018.8517436 ↗
29. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
30. Li, R., and Yang, J. (2018, January 10–12). Improved YOLOv2 object detection model. Proceedings of the 2018 6th International Conference on Multimedia Computing and Systems (ICMCS), Rabat, Morocco.10.1109/ICMCS.2018.8525895 ↗
31. Liang, Z., Shao, J., Zhang, D., and Gao, L. (2018, January 21–22). Small object detection using deep feature pyramid networks. Proceedings of the Advances in Multimedia Information Processing–PCM 2018: 19th Pacific-Rim Conference on Multimedia, Hefei, China. Proceedings, Part III 19.10.1007/978-3-030-00764-5_51 ↗
32. A new lightweight deep neural network for surface scratch detection10.1007/s00170-022-10335-8 ↗
33. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.10.1007/978-3-319-46448-0_2 ↗
34. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
35. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
36. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
37. Jocher, G. (2024, March 07). Ultralytics YOLOv5. Available online: https://github.com/ultralytics/yolov5.
38. Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., and Chu, X. (2023). Yolov6 v3. 0: A full-scale reloading. arXiv.
39. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.10.1109/CVPR52729.2023.00721 ↗
40. Jocher, G., Chaurasia, A., and Qiu, J. (2024, May 04). YOLO by Ultralytics. Available online: https://github.com/ultralytics/ultralytics.
41. A real-time object detection algorithm for video10.1016/j.compeleceng.2019.05.009 ↗
42. Huang, R., Pedoeem, J., and Chen, C. (2018, January 10–13). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.10.1109/BigData.2018.8621865 ↗
43. A novel finetuned YOLOv6 transfer learning model for real-time object detection10.1007/s11554-023-01299-3 ↗
44. Bi-path combination YOLO for real-time few-shot object detection10.1016/j.patrec.2022.11.025 ↗
45. RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring10.1007/s10489-021-02893-3 ↗
46. Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments10.1109/ACCESS.2019.2961959 ↗
47. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2021, January 20–25). Scaled-YOLOv4: Scaling Cross Stage Partial Network. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01283 ↗
48. Ganesh, P., Chen, Y., Yang, Y., Chen, D., and Winslett, M. (2022, January 3–8). YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs. Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.10.1109/WACV51458.2022.00138 ↗
49. Wang, T., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., and Shao, L. (November, January 27). Learning Rich Features at High-Speed for Single-Shot Object Detection. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00206 ↗
50. Wang, R.J., Li, X., and Ling, C.X. (2018, January 3–8). Pelee: A Real-Time Object Detection System on Mobile Devices. Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, Montreal, QC, Canada.
51. Lee, Y., Hwang, J.w., Lee, S., Bae, Y., and Park, J. (2019, January 16–17). An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.10.1109/CVPRW.2019.00103 ↗
52. Law, H., Teng, Y., Russakovsky, O., and Deng, J. (2019). Cornernet-lite: Efficient keypoint based object detection. arXiv.
53. Qin, Z., Li, Z., Zhang, Z., Bao, Y., Yu, G., Peng, Y., and Sun, J. (November, January 27). ThunderNet: Towards real-time generic object detection on mobile devices. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.
54. Real-Time Object Detection With Reduced Region Proposal Network via Multi-Feature Concatenation10.1109/TNNLS.2019.2929059 ↗
55. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv.
56. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23–28). End-to-End Object Detection with Transformers. Proceedings of the Computer Vision—ECCV 2020, Glasgow, UK.10.1007/978-3-030-58452-8_13 ↗
57. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10–17). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.10.1109/ICCV48922.2021.00986 ↗
58. Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14–19). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.10.1109/CVPRW50498.2020.00203 ↗
59. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
60. Tan, M., and Le, Q. (2019, January 9–15). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA.
61. Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2021, January 20–25). Sparse r-cnn: End-to-end object detection with learnable proposals. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01422 ↗
62. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20–25). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
63. Yu, W., Si, C., Zhou, P., Luo, M., Zhou, Y., Feng, J., Yan, S., and Wang, X. (2022). Metaformer baselines for vision. arXiv.
64. Fang, Y., Sun, Q., Wang, X., Huang, T., Wang, X., and Cao, Y. (2023). Eva-02: A visual representation for neon genesis. arXiv.10.2139/ssrn.4813567 ↗
65. Smith, L.N., and Topin, N. (2019, January 15–17). Super-convergence: Very fast training of neural networks using large learning rates. Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Baltimore, MD, USA.10.1117/12.2520589 ↗