![]() ![]() ![]() “Using AI in waste management promises further potential. Jinlu Technology trains an image-segmentation model using Paddle Detection, a PaddlePaddle toolkit for image processing, to identify plastic bottles-in an effort to make waste sorting more efficient. While traditional algorithm-accuracy screening stays between 60% and 90%, depending on the quality of the garbage, deep-learning algorithms deliver an accuracy of 93% to 99%. The model predicts on Edgeboard (PaddlePaddle’s edge computing development platform) through Paddle Lite, PaddlePaddle’s deep-learning framework tailored for lightweight models, and sends signals to robotic arms that classify the garbage. The model takes just half a second to recognize an image.įor plastic bottles, Jinlu Technology trains an instance-segmentation model using Paddle Detection, a PaddlePaddle toolkit for image processing. It also uses an image-segmentation model to find garbage and do things like detect the edge of a bottle and determine its center point. Jinlu Technology uses a garbage-sorting robot programmed with an object-detection model to identify different types of garbage. Although the industry lacks the expertise of deep learning, with PaddlePaddle, developers don’t necessarily have to be deep-learning experts or build things like data-processing models from scratch. Complications tend to arise with the detection quality and with identifying diverse garbage,” says Zhang.Ī computer-vision veteran, Zhang was eyeing PaddlePaddle to develop applications for improving waste sorting in China. “The garbage in China is not compatible with what can be detected by this technology. “Using traditional computer-vision models in China would be useless,” says Zhiwen Zhang, CEO of Jinlu Technology. But the task is not as efficient in all countries. In Europe and the US, computer-vision technology has been extensively used for detecting different types of waste, such as glass, plastic, and cardboard, to make waste sorting more efficient. Collecting it and separating it exposes waste pickers to any number of risk factors and hazards, making this a critical area for the development of innovative AI technologies. According to a World Bank report, more than 2 billion tons of municipal solid waste are produced in the world each year. For example, in waste management, AI is transforming refuse picking, sorting, and recycling, supporting efforts to conserve natural resources, reduce carbon emissions, and lessen waste going into landfill sites. ![]() How PaddlePaddle trained garbage-sorting robotsĭeep-learning technologies create opportunities for revamping operations, workload management, and productivity, even in traditional industries such as manufacturing, forestry, energy, and waste management. At the recent Baidu Deep Learning Developer Conference Wave Summit 2020, Chief Technology Officer Haifeng Wang announced PaddlePaddle’s collaboration in a hardware ecosystem that includes leading global tech companies such as Intel, NVIDIA, Arm China, and Huawei. ![]()
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