Skip to main content

Advertisement

Log in

SSD based waste separation in smart garbage using augmented clustering NMS

  • Published:
Automated Software Engineering Aims and scope Submit manuscript

Abstract

Object detection plays a pivotal role in autonomous systems helps to build the machines to be intelligent as humans that leads to build an Artificial Intelligence application used for society,Industries,face-recognition and so-on. Nowadays, it is difficult to manage the waste generated by humans and industries which is increasing rapidly day by day needs to address the problem to make automation for separating the Bio-degradable and Non-Bio degradable waste. Although humans are tried to manage impact of waste management in society to maintain the eco-system by implementing a separate trash for Bio and Non-bio waste. Sometimes it is difficult to follow for the separation of waste manually by humans. There is no existing sensor to identify the types of wastes. In this proposed system, it evolves the implementation of the bio-degradable and non-bio degradable object detection method to help to detect these objects automatically with the augmented clustering NMS using Single-shot detector methods. The enhanced augmented clustering algorithm effectively detects the multiple objects in the video along with the respective bio or non-bio classification custom object detection model. With the build thousand images for each class custom dataset model to train the objects using deep learning neural network. A custom object detection data model is built with the help of NVIDIA GPU RTX 4 GB using tensorflow model. Here the results are interpreted with the mean average precision value of 0.965 with ACNMS Single shot object detector which is effectively detected with the new enhanced technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Andrew Howe, J.: Improved Clustering with Augmented k-means. 1, 1–18 (2017)

  • Annepu, R, K.: Sustainable Solid Waste Management in India (Columbia: Columbia University, 2012), pp. 3–7

  • Bodla, N., Singh, B., Chellapa, R., Davis, L.S.: Soft-NMS-Improving object detection with one line of code. IEEE Int. Conf. Comput. vis. 1, 1–9 (2017)

    Google Scholar 

  • Chinnaswamy, S., Annapurani, K.: Trust aggregation authentication protocol using machine learning for IOT wireless sensor networks. Comput. Electr. Eng. 91, 1–15 (2021)

    Article  Google Scholar 

  • Dersch, S., Heurich, M., Krueger, N., Krzystek, P.: Combining graph- cut clustering with object-based stem detection for tree segmentation in highly dense airborne lidar point clouds. ISPRS J Photogram Remote Sens 172, 207–222 (2021)

    Article  Google Scholar 

  • Devaki, P., Shivarsha, S., Bala Kowsalya, G., Manjupavithra, M.: Real-time object detection using deeplearning and open CV. Int. J. Innov. Technol. Explor. Eng. 12(8), 411–414 (2019)

    Google Scholar 

  • Fathimah, Y.A., Widianto, A., Hanafi, M.: Cyber physical System enabled in suitable waste management 4.0: a smart waste collection system for indonesian semi-urban cities. Proc. Manuf. 43, 535–542 (2020)

    Google Scholar 

  • Harsh Panwar, P.K., Gupta and Mohammad Khuebeb Siddiqui, : Aqua Vision: automating the detection of waste in water bodies using deep transfer learning. Case Stud. Chem. Environ. Eng. 2, 1–5 (2020)

    Google Scholar 

  • Hespeler, S.C., Nemati, H., Niri, E.D.: Non-Destructive thermal imaging for object detection for robotic inspection and harvesting of chilli Peppers. Artif. Intell. Agricul. 5, 102–117 (2021)

    Google Scholar 

  • Kim, Y., Lee, M., Kim, S.B.: Swarm ascending: Swarm intelligence-based exemplar group detection for robust clustering. Appl. Soft Comput. J. 102, 1–16 (2021)

    Article  Google Scholar 

  • Kumar, A., Agrawal, A.: Recent trends in solid waste management status, challenges and potential for the future Indian Cities-a review. Curr. Res. Environ. Sustain. 2, 1–19 (2020)

    Article  Google Scholar 

  • Kumar, Ai., Srivastava, S.: Object detection system based on convolution neural networks using single-shot multi box detector. Proc. Comput. Sci. 170, 2610–2617 (2020)

    Article  Google Scholar 

  • Kumar, A., Zhang, Z.J., Lyu, H.: Object detection in real time based on improved single-shot multi box detector algorithm. Eur. J. Wirel. Commun. Netw. 204, 1–18 (2020)

    Google Scholar 

  • Liu, W., Anguelov, D., Erhan, D.: SSD-Single shot multibox Detector. Springer AG, 1:1–17 (2016)

  • Liu, Y., Sun, P., N wergeles and Y Shang, : A survey and performance evalution of deep learning methods for small object detection. Expert Syst. Appl. 172, 1–14 (2021)

    Article  Google Scholar 

  • Li, Y., Dong, H., Li, H., Zhang, X.: Multi-block SSD based on small object detection for UAV railway scene surveillance. Chin. J. Aeronaut. 33(6), 1747–1755 (2020)

    Article  Google Scholar 

  • Li, C.-J., Qu, Z., S ye and Wang LL, : A method of cross-layer fusion multi-object detection and recognition based on improved faster R-CNN model in complex traffic environment. Pattern Recognit. Lett. 145, 127–134 (2021)

    Article  Google Scholar 

  • Lopes, A.C.P., Senfter, T., Ebner, C.: Separation of biodegradable material from the low calorific fraction of municipal solid waste. J. Clean. Prod. 280, 1–11 (2021)

  • Lyu, Y., Yang, M.Y., Vosselman, G., Xia, G.-S.: Video object detection with a convolutional regression tracker. ISPRS J. Photogram. Remote Sens. 172, 139–150 (2021)

    Article  Google Scholar 

  • Ma, G., Wu, M., Wu, Z., Yang, W.: Single –Shot multibox detector and building information modeling-based quality inspection model for construction projects. J. Build. Eng. 38, 1–14 (2021)

    Google Scholar 

  • Murthy, C.B., Hashmi, M.F.: Investigations of object detections in images/videos using various deep learning techniques and embedded platforms-a comprehensive review. Appl. Sci. 9(10), 3280–3296 (2020)

    Article  Google Scholar 

  • Nagrath, P., Jain, R., Madan, A.: SSDMNV2: a real time DNN-based face Mask detection system using single shot multibox detector and MobileNetV2. Sustain. Cities Soc. 66, 1–11 (2021)

    Article  Google Scholar 

  • Nguyen, H.T., Lee, E.-H., Bae, C.H., Lee, S.: Multiple object detection based on clustering and deep learning methods. Sensors 20(16), 4424–4438 (2020)

    Article  Google Scholar 

  • Nowakowski, P., Pamula, T.: Application of deep learning object classifier to improve e-waste collection planning. Waste Manag. 109, 1–9 (2020)

    Article  Google Scholar 

  • Qin, J., Xu, N.: Research and implementation of social distance monitioring technology based on SSD. Proc. Comput. Sci. 183, 768–775 (2021)

    Article  Google Scholar 

  • Sahoo, P.K., Kanungo, P., Mishra, S.: Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder sequences. J. King Saud Univ. Comput. Inform. Sci. 1, 1–9 (2020)

    Google Scholar 

  • ShanFur, B.B., Verl, A.: Prediction of configuration of objects in a bin based on synthetic sensor data. Proc. CIRP 88, 54–59 (2020)

    Article  Google Scholar 

  • Sohag, M., Podder, A.K.: Smart garbage management system for a sustainable urbanlife: an IOT based application. Internet Things 11, 2542–2555 (2020)

    Article  Google Scholar 

  • Srinivasan, R., Subalalitha, C.N.: Sentimental analysis from imbalanced code-mixed data using machine learning approaches. Distrib. Parallel Databases 1, 1–16 (2021)

    Google Scholar 

  • Tapas Kumar Ghatak: Municipal solid waste management in india: a few unaddressed issues. Int. Conf. Solid Waste Manag. Proc. Comput. Sci. 35, 169–175 (2016)

    Google Scholar 

  • Wang, Z., Li, H., Yang, X.: Vision based robotic system for on-site construction and demolition waste sorting and recycling. J. Build. Eng. 32, 1–13 (2020)

    Google Scholar 

  • Xianzhi, D., Lin, T.-Y., Pengchong jin, : Spine net: learning scale-permuted backbone for recognition and localization. IEEE Exp. 6, 11589–11598 (2020)

    Google Scholar 

  • Younis, A., Shixin, L., Shelembi JN and Zhang Hai, : Real-time object detection using pre- trained deep learning models Mobilenet-SSD. ICCDE ACM 1, 44–48 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Karthikeyan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karthikeyan, M., Subashini, T.S. & Jebakumar, R. SSD based waste separation in smart garbage using augmented clustering NMS. Autom Softw Eng 28, 17 (2021). https://doi.org/10.1007/s10515-021-00296-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10515-021-00296-9

Keywords

Navigation