Landslide is a very serious geological disaster. When the heavy rain occurs in the area with loose soil layer, the mountain becomes loose after strong erosion of rain water. As soon as the threshold of adhesion force between mud and mountain base is broken, the powerful harmful force formed by landslides will bury houses, people and cars. Traditional detection methods, represented by InSAR or LIDAR, were capable of identifying landslides to a certain extent, but in-time and real-time performance were not good enough. In this paper, an intelligent identification method based on convolutional neural network(CNN) is proposed, which can in-time and real-time to identify landslides, thus greatly reducing the loss of life, health and property.
The detection of microorganisms like bacteria in water is extremely important and challenging. Colony detection is an effective solution because visible colonies can be formed by bacteria in water. Aiming at the characteristics of small targets and regular shape in RGB image, a cascade network combining SSD_MOBILENET_V1_FPN with SVM HOG classifier is designed to detect and count the colonies with high accuracy, both error rate and missing rate were less than 3%. To solve the performance bottleneck, a on-chip system device Zynq UltraScale+ MPSoC EV is applied to accelerate colony count cascade network based on FPGA which could support AI computing acceleration, it can detect and count 10 colony images per second with the advantage of portability and low power consumption.
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