It should be possible with a combination of object detection, monocular depth estimation (for example Monodepth, Godard et al.) and some math:

1. Apply object detection/segmentation model to your image to find bounding boxes
2. Determine the position of the center of the bounding box in the camera FOV, you could calculate a relative position here via: $\frac{\text{bbox}_x}{\text{img-width}} = p_x$
3. Apply monocular depth estimation to generate a depth map from your camera image
4. Take the bounding boxes to cut out regions in the depth maps and compute the average distance of the object to the camera lens.

Now you have: relative x-position in FOV $p_x$ and distance $d$, which gives you a vector in radial coordinates from your camera lens $\mathbf{\hat{p}} = (p_x, d)$ that directly translates to a position in the first image you showed.

It should be possible with a combination of object detection, monocular depth estimation (for example Monodepth, Godard et al.) and some math. Here is an example of how this could look like:

1. Apply object detection/segmentation model to your image to find bounding boxes
2. Determine the position of the center of the bounding box in the camera FOV, you could calculate a relative position here via: $\frac{\text{bbox}_x}{\text{img-width}} = p_x$
3. Apply monocular depth estimation to generate a depth map from your camera image
4. Take the bounding boxes to cut out regions in the depth maps and compute the average distance of the object to the camera lens.

Now you have: relative x-position in FOV $p_x$ and distance $d$, which gives you a vector in radial coordinates from your camera lens $\mathbf{\hat{p}} = (p_x, d)$ that directly translates to a position in the first image you showed.