Apparatus and method for object detection

1 . An object detection apparatus comprising:

a sensor device configured to obtain an image; and

a processor configured to

obtain a first bounding box and a second bounding box based on a learning result of the image,

calculate a two-dimensional overlapping degree of between the first bounding box and the second bounding box,

calculate a depth deviation between the first bounding box and the second bounding box, and

perform a non-maximum suppression procedure based on a three-dimensional overlapping degree,

wherein the processor is further configured to:

obtain a normalization factor by calculating a difference between “1” and a normalized depth deviation; and

obtain the three-dimensional overlapping degree by performing a multiplication operation between the two-dimensional overlapping degree and the normalization factor.

2 . The object detection apparatus of claim 1 , wherein the processor is configured to select the first bounding box and the second bounding box within output values of a network using an anchor box corresponding to two or more vehicle sizes.

3 . The object detection apparatus of claim 2 , wherein the processor is configured to:

calculate the two-dimensional overlapping degree of between the first bounding box and the second bounding box;

determine a two-dimensional region of the first bounding box and a two-dimensional region of the second bounding box on an x-y plane of an input image; and

calculate an overlapping degree between the two-dimensional region of the first bounding box and the two-dimensional region of the second bounding box, and

wherein an x-axis of the x-y plane is perpendicular to a traveling direction of a vehicle on a road surface, and wherein a y-axis of the x-y plane is an axis perpendicular to the road surface.

4 . The object detection apparatus of claim 3 , wherein the processor is configured to obtain the depth deviation by calculating a difference in coordinate values on a z-axis between a reference point of the first bounding box and a reference point of the second bounding box on a bird's eye view of the image including the z-axis perpendicular to the x-y plane.

5 . The object detection apparatus of claim 4 , wherein the processor is configured to calculate the three-dimensional overlapping degree based on an absolute value of the depth deviation.

6 . The object detection apparatus of claim 5 , wherein the processor is configured to normalize the depth deviation within a range greater than 0 and less than 1, based on a ratio of the depth deviation to a maximum deviation of a depth value.

7 . The object detection apparatus of claim 6 , wherein the processor is configured to:

calculate an average depth value for each type of the anchor box; and

obtain a maximum deviation of the depth value by calculating a difference between a maximum value of the average depth value and a minimum value of the average depth value.

8 . The object detection apparatus of claim 6 , wherein the processor is configured to set a size of the three-dimensional overlapping degree to be small in inverse proportion to the depth deviation.

9 . The object detection apparatus of claim 1 , wherein the processor is configured to exclude a bounding box having a low confidence among the first bounding box and the second bounding box from an object candidate group when the three-dimensional overlapping degree is equal to or greater than a preset threshold value.

10 . An object detection method comprising:

obtaining a first bounding box and a second bounding box based on a learning result of an input image;

calculating a two-dimensional overlapping degree between the first bounding box and the second bounding box;

calculating a depth deviation between the first bounding box and the second bounding box; and

performing a non-maximum suppression procedure based on a three-dimensional overlapping degree,

wherein calculating the three-dimensional overlapping degree includes:

obtaining a normalization factor by calculating a difference between “1” and a normalized depth deviation; and

obtaining the three-dimensional overlapping degree by performing a multiplication operation between the two-dimensional overlapping degree and the normalization factor.

11 . The object detection method of claim 10 , wherein obtaining the first bounding box and the second bounding box includes selecting a pair of bounding boxes within output values of a network using an anchor box corresponding to two or more vehicle sizes.

12 . The object detection method of claim 11 , wherein

the input image includes a plane defined by an x-axis and a y-axis;

calculating the two-dimensional overlapping degree between the first bounding box and the second bounding box includes

determining a two-dimensional region of the first bounding box and a two-dimensional region of the second bounding box on an x-y plane, and

calculating an overlapping degree between the two-dimensional region of the first bounding box and the two-dimensional region of the second bounding box;

the x-axis of the x-y plane is perpendicular to a traveling direction of a vehicle on a road surface; and

the y-axis of the x-y plane is an axis perpendicular to the road surface.

13 . The object detection method of claim 12 , wherein

calculating the depth deviation includes obtaining the depth deviation by calculating a difference in coordinate values on a z-axis between a reference point of the first bounding box and a reference point of the second bounding box on a bird's eye view of the input image, and

the z-axis is perpendicular to the x-y plane.

14 . The object detection method of claim 13 , wherein obtaining the depth deviation further includes obtaining an absolute value of the depth deviation.

15 . The object detection method of claim 14 , wherein obtaining the depth deviation further includes normalizing the depth deviation within a range greater than 0 and less than 1, based on a ratio of the depth deviation to a maximum deviation of a depth value.

16 . The object detection method of claim 15 , wherein obtaining the depth deviation further includes:

calculating an average depth value for each of types of the anchor box; and

obtaining a maximum deviation of the depth value by calculating a difference between a maximum value of the average depth value and a minimum value of the average depth value.

17 . The object detection method of claim 15 , wherein calculating the three-dimensional overlapping degree includes setting a size of the three-dimensional overlapping degree to be small in inverse proportion to the depth deviation.

18 . The object detection method of claim 10 , wherein performing the non-maximum suppression procedure includes excluding a bounding box having a low confidence among the first bounding box and the second bounding box from an object candidate group when the three-dimensional overlapping degree is equal to or greater than a preset threshold value.