Similar articles

Abstract: An experimental machine vision apparatus was used to identify and extract recyclable plastic bottles out of a conveyor belt. Color images were taken with a commercially available Webcam, and the recognition was performed by our homemade software, based on the shape and dimensions of object images. The software was able to manage multiple bottles in a single image and was additionally extended to cases involving touching bottles. The identification was fulfilled by comparing the set of measured features with an existing database and meanwhile integrating various recognition techniques such as minimum distance in the feature space, self-organized maps, and neural networks. The recognition system was tested on a set of 50 different bottles and provided so far an accuracy of about 97% on bottle identification. The extraction of the bottles was performed by means of a pneumatic arm, which was activated according to the plastic type; polyethylene-terephthalate (PET) bottles were left on the conveyor belt, while non-PET bottles were extracted. The software was designed to provide the best compromise between reliability and speed for real-time applications in view of the commercialization of the system at existing recycling plants.

[1] Agante, E., Rodrigues, I., Carvalho, T., 2006. Separation of PET from PVC by Column Flotation. Sohn Int. Symp. on TMS Fall Extraction and Processing Division, 6:331-340.

[2] Coltro, L., Gasparino, B.F., Queiroz, G.D.C., 2008. Plastic material recycling: the importance of the correct identification. Polimeros, 18(2):119-125.

[3] Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing. Prentice-Hall, Upper Saddle River.

[4] Guha, D., 2006. Plastic recycling. Chem. Eng. World, 41(5):43.

[5] Hearn, G.L., Ballard, J.R., 2005. The use of electrostatic techniques for the identification and sorting of waste packaging materials. Resour. Conserv. Recycl., 44(1):91-98.

[6] Kopardekar, P., Mital, A., Anand, S., 1993. Manual, hybrid and automated inspection literature and current research. Integr. Manuf. Syst., 4(1):18-29.

[7] Mital, A., Govindaraju, M., Subramani, B., 1998. A comparison between manual and hybrid methods in parts inspection. Integr. Manuf. Syst., 9(6):344-349.

[8] Scavino, E., Wahab, D.A., Basri, H., Mustafa, M.M., Hussain, A., 2007. An efficient segmentation technique for known touching objects using a genetic algorithm approach. LNCS, 4830:786-790.

[9] Schut, J.H., 2007. ‘Green’ and growing. Plast. Technol., 53(11):112.

[10] Scott, D.M., 1995. A two-color near infrared sensor for sorting recycled plastic waste. Meas. Sci. Technol., 6(2):156-159.

[11] Siddique, R., Khatib, J., Kaur, I., 2008. Use of recycled plastic in concrete: a review. Waste Manag., 28(10):1835-1852.

[12] Stewart, R., 2008. Going green. Plast. Eng., 64(1):16-22.

[13] Tachwali, Y., Al-Assaf, Y., Al-Ali, A.R., 2007. Automatic multistage classification system for plastic bottles recycling. Resour. Conserv. Recycl., 52(2):266-285.

[14] Taylor, B., 2006. Visions of plastic. Recycl. Today, 44(4):48-56.

[15] Wahab, D.A., Hussain, A., Scavino, E., Mustafa, M.M., Basri, H., 2006. Development of a prototype automated sorting system for plastic recycling. Am. J. Appl. Sci., 3(7):1924-1928.

[16] Wahab, D.A., Abidin, A., Azhari, C.H., 2007. Recycling trends in the plastics manufacturing and recycling companies in Malaysia. J. Appl. Sci., 7(7):1030-1035.