| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Object detection aims to find the objects which people are interested in, and findthese objects’ position and category. The applications of object detection includemachine vision, factory automation, and electric car.The data structure of bounding box in traditional object detection network limitsobjects’ center in certain range. Therefore, only one grid can predict one objectcorrectly. This limits the training method, and may miss some good method to trainbetter object detection network. In this thesis, a new data structure of boundingbox is proposed, and make bounding box get rid of the limit of objects’ center.By applying this data structure of bounding box, many grid can predict one objectcorrectly. Based on th new data structure, a new training method is proposed in thisthesis. This training method helps to reduce the difficulty of training objects whosecenters are near boundary of grids. Then the feature of network model can serveother hard training object. Therefore, the proposed training method can detectsome objects that can’t be detected in old training method.What’s more, not only the new data structure of bounding box makes no sideeffect in old training method, but also adapt to new training method better. In oldtraining method, old data structure gets intersection over union(IoU) 88.8% withtest data, and new data structure gets IoU 89.4%. In new training method, old datastructure gets IoU 87.9%, and new data structure gets IoU 89.6%.
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