Background Research on early object detection methods of crop diseases and pests in the natural environment has been an important research direction in the fields of computer vision, complex image processing and machi...
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Background Research on early object detection methods of crop diseases and pests in the natural environment has been an important research direction in the fields of computer vision, complex image processing and machine learning. Because of the complexity of the early images of tomato diseases and pests in the natural environment, the traditional methods can not achieve real-time and accurate detection. Results Aiming at the complex background of early period of tomato diseases and pests image objects in the natural environment, an improved object detectionalgorithm based on YOLOv3 for early real-timedetection of tomato diseases and pests was proposed. Firstly, aiming at the complex background of tomato diseases and pests images under natural conditions, dilated convolution layer is used to replace convolution layer in backbone network to maintain high resolution and receptive field and improve the ability of small object detection. Secondly, in the detection network, according to the size of candidate box intersection ratio (IOU) and linear attenuation confidence score predicted by multiple grids, the obscured objects of tomato diseases and pests are retained, and the detection problem of mutual obscure objects of tomato diseases and pests is solved. Thirdly, to reduce the model volume and reduce the model parameters, the network is lightweight by using the idea of convolution factorization. Finally, by introducing a balance factor, the small object weight in the loss function is optimized. The test results of nine common tomato diseases and pests under six different background conditions are statistically analyzed. The proposed method has a F1 value of 94.77%, an AP value of 91.81%, a false detection rate of only 2.1%, and a detectiontime of only 55 Ms. The test results show that the method is suitable for early detection of tomato diseases and pests using large-scale video images collected by the agricultural Internet of Things. Conclusions At present, most of
This paper presents a real-time detection algorithm which detects abandoned objects in embedded Intelligent Video Surveillance (IVS) System. This algorithm uses two different Gaussian mixture models (GMM) with differe...
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ISBN:
(纸本)9781479960798
This paper presents a real-time detection algorithm which detects abandoned objects in embedded Intelligent Video Surveillance (IVS) System. This algorithm uses two different Gaussian mixture models (GMM) with different learning rates to extract the foreground in order to detect the abandoned objects and output alarms. Experimental results show the algorithm is robust and well-performed in different circumstances.
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