物件偵測(object detection)的目地是找出感興趣的物件種類，並標出物件的位置。常見的應用有機器視覺、工廠自動化、電動車等。傳統的物件偵測網路其物件邊界框的中心位置被限制在一定的範圍內，導致訓練時只有一個網格可以正確預測出一個物件。這種訓練方法限制住了訓練的方法，減少訓練出更好物件偵測網路的可能性。本論文提出新的物件邊界框資料結構，可以完全擺脫物件中心的限制，使多個網格都能預測同一物件。基於新的邊界框資料結構，本論文提出另一種訓練方法，此訓練方法有助於降低物件中心位於網格邊緣時的訓練難度，使網路模型的特徵能夠服務其他有難度的物件，因此能偵測出一些舊訓練方法會偵測不到的物件。除此之外，新的邊界框資料結構不但在舊的訓練方法中沒有副作用，還更能適應新的訓練方法。在舊的訓練方法中舊邊界框資料結構的邊界框重合度(intersection over union, IoU)為88.8%，新的邊界框資料結構則是89.4%；在新的訓練方法中，舊的邊界框資料結構的邊界框重合度則是87.9%，新的邊界框資料結構為89.6%。

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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