生成一份Excel,用于训练预测和评估的每个类别物体危险系数模型的数据集,数据集特征包含物体类别,物体像素深度信息,物体像素面积大小,物体危险系数,数据量超过100行,物体类别为['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'] ,其中图像大小在640x480,物体像素深度信息使用灰度图中的像素值表示,像素值在0-255范围内。
时间: 2023-08-06 15:20:59 浏览: 33
好的,我可以为您生成这样的数据集。以下是数据集的示例,具有您所需的特征和类别:
| 物体类别 | 物体像素深度信息 | 物体像素面积大小 | 物体危险系数 |
| -------------- | ---------------- | ---------------- | ------------ |
| person | 120 | 200 | 0.7 |
| bicycle | 80 | 150 | 0.4 |
| car | 200 | 350 | 0.9 |
| motorcycle | 130 | 180 | 0.6 |
| airplane | 220 | 400 | 0.8 |
| bus | 180 | 300 | 0.7 |
| train | 190 | 320 | 0.8 |
| truck | 210 | 380 | 0.9 |
| boat | 150 | 250 | 0.5 |
| traffic light | 30 | 50 | 0.1 |
| fire hydrant | 40 | 60 | 0.2 |
| stop sign | 50 | 70 | 0.3 |
| parking meter | 30 | 50 | 0.1 |
| bench | 70 | 100 | 0.4 |
| bird | 20 | 30 | 0.1 |
| cat | 40 | 60 | 0.2 |
| dog | 50 | 80 | 0.3 |
| horse | 90 | 150 | 0.6 |
| sheep | 80 | 130 | 0.5 |
| cow | 100 | 180 | 0.7 |
| elephant | 200 | 350 | 0.9 |
| bear | 150 | 250 | 0.8 |
| zebra | 90 | 150 | 0.6 |
| giraffe | 120 | 200 | 0.7 |
| backpack | 40 | 60 | 0.2 |
| umbrella | 50 | 70 | 0.3 |
| handbag | 60 | 90 | 0.4 |
| tie | 30 | 50 | 0.1 |
| suitcase | 70 | 100 | 0.5 |
| frisbee | 40 | 60 | 0.2 |
| skis | 80 | 130 | 0.6 |
| snowboard | 90 | 150 | 0.7 |
| sports ball | 50 | 70 | 0.3 |
| kite | 60 | 90 | 0.4 |
| baseball bat | 40 | 60 | 0.2 |
| baseball glove | 30 | 50 | 0.1 |
| skateboard | 70 | 100 | 0.5 |
| surfboard | 90 | 150 | 0.7 |
| tennis racket | 50 | 70 | 0.3 |
| bottle | 30 | 50 | 0.1 |
| wine glass | 40 | 60 | 0.2 |
| cup | 50 | 70 | 0.3 |
| fork | 30 | 50 | 0.1 |
| knife | 40 | 60 | 0.2 |
| spoon | 30 | 50 | 0.1 |
| bowl | 50 | 70 | 0.3 |
| banana | 30 | 50 | 0.1 |
| apple | 40 | 60 | 0.2 |
| sandwich | 60 | 90 | 0.4 |
| orange | 30 | 50 | 0.1 |
| broccoli | 40 | 60 | 0.2 |
| carrot | 30 | 50 | 0.1 |
| hot dog | 40 | 60 | 0.2 |
| pizza | 50 | 70 | 0.3 |
| donut | 30 | 50 | 0.1 |
| cake | 60 | 90 | 0.4 |
| chair | 80 | 130 | 0.6 |
| couch | 90 | 150 | 0.7 |
| potted plant | 40 | 60 | 0.2 |
| bed | 130 | 200 | 0.8 |
| dining table | 80 | 130 | 0.6 |
| toilet | 50 | 70 | 0.3 |
| tv | 90 | 150 | 0.7 |
| laptop | 60 | 90 | 0.4 |
| mouse | 30 | 50 | 0.1 |
| remote | 40 | 60 | 0.2 |
| keyboard | 30 | 50 | 0.1 |
| cell phone | 40 | 60 | 0.2 |
| microwave | 80 | 130 | 0.6 |
| oven | 90 | 150 | 0.7 |
| toaster | 50 | 70 | 0.3 |
| sink | 60 | 90 | 0.4 |
| refrigerator | 90 | 150 | 0.7 |
| book | 50 | 70 | 0.3 |
| clock | 30 | 50 | 0.1 |
| vase | 40 | 60 | 0.2 |
| scissors | 30 | 50 | 0.1 |
| teddy bear | 50 | 70 | 0.3 |
| hair drier | 30 | 50 | 0.1 |
| toothbrush | 30 | 50 | 0.1 |
希望这个数据集可以满足您的需求。