| Summary: | 碩士 === 國立交通大學 === 電子研究所 === 106 === Planthopper is a kind of rice pest that travels from Philippines to Taiwan every year. These pests are able to polish off rice in a short time and spread quickly to a large area. Moreover, they carry some sort of disease that makes rice sick. When they travel from one place to another, the disease spreads along these areas and cause huge damage to the agricultural industry. For these reasons, it is urgent to construct a system that is capable of detecting rice pests in time.
Our detection model consists of two stages. At the first stage, traditional image processing technique conducts to detect the main part of the plant. We reserve this part and discard the remaining of the image. At the second stage, we use the convolutional neural network to detect pests. The model is based on the Single Shot Multi-box Detector which performs classification and localization at the same time. However, SSD has a serious problem for discarding too much background information at the max pooling layers. It is easy for the model to misrecognize reflected light as positive due to their similar shape and color. To solve this problem, we introduce a new way of pooling. Instead of reserving the max value, our model intends to save the local difference. This model can reserve more background information and obtain better results when evaluating.
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