Development of aerial photographing system with throwing device for disaster information gathering
Recently, as a disaster information gathering tool, aerial photographing devices which gather photos and videos from the air get much attention. Especially, the aerial photographing method at low altitude below 15 meters is important to the disaster information gathering. As these devices, there are helium gas balloons, radio-controlled helicopters, and pole/crane vehicles. However, these devices have risks that trigger accidental contact and crackup by air stream at low airspace. In order to improve the safety of these devices, in this paper, we have developed a throw type photographing system which consists of an automatically photographing device packaged in a plastic capsule egg and a launching device. The attitude of the photographing device is controlled by the microcomputer with acceleration, gyro and compass sensors. These sensors also used to estimate the time when the device reaches the top of air. Moreover, we have shown the effectiveness of the proposed system by shooting aerial photographs in practice.
Development of throw type automatic photographing device
In disaster, it is important to photographs and video from the sky. Aerial photographing devices such as helium gas balloons and helicopters have ever been used for information gathering activities at that time. However, these devices have a high risk because they are strongly affected by air stream at low airspace. As the new photographing device improving the above problem, in this paper, we have developed a throw type photographing device packaged in a plastic capsule egg, and proposed an automatic photographing system. The photographing device is controlled by the control circuit with an accelerometer and takes a photograph when it reaches the top of air. The accelerometer is used to calculate the time that the device until photographing. Moreover, we take aerial photographs in practice and show the effectiveness of the proposed system.
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.