METHOD AND APPARATUS FOR COMPUTER VISION BASED ON NEURAL EXPOSURE FUSION FOR HIGH-DYNAMIC RANGE OBJECT DETECTION
Departing from conventional HIDR image fusion approach, a learned task-driven fusion in the feature domain is disclosed. Instead of using a single companded image, the disclosed method exploits semantic features from all exposures learned in an end-to-end fashion with supervision from downstream detection losses. The method outperforms all tested conventional HDR exposure fusion and auto-exposure methods in challenging automotive HIDR scenarios.
1 . A method of detecting objects from camera-produced images comprising:
generating multiple raw exposure-specific images for a scene;
performing for the multiple raw exposure-specific images respective processes of image enhancement to produce respective processed exposure-specific images;
extracting from the processed exposure-specific images respective sets of exposure-specific features collectively constituting a superset of features;
identifying, using the respective sets of exposure-specific features, exposure-specific sets of candidate objects; and
fusing the exposure-specific sets of candidate objects to form a fused set of candidate objects.
2 . The method of claim 1 , wherein the respective processes of image enhancement include one or more of contrast stretching, demosaicing, resizing, a power transform, color correction, threshold unsharp mask filtering, affine transform, or learned gamma correction.
3 . The method of claim 1 , wherein the respective processes of image enhancement include:
applying a first color space transform to Y, Cb, Cr color space;
executing a denoising filter in the Y, Cb, Cr color space; and
applying a second color space transform to RGB color space.
4 . The method of claim 1 , wherein extracting the respective sets of exposure-specific features includes employing a ResNet neural network to generate the respective sets of exposure-specific features.
5 . The method of claim 1 , wherein extracting the respective sets of exposure-specific features includes encoding a presence of wheels, headlights, glass texture, or metal texture among the respective sets of exposure-specific features.
6 . The method of claim 1 , wherein identifying the exposure-specific sets of candidate objects includes computing respective bounding boxes for the exposure-specific set of candidate objects.
7 . The method of claim 1 , wherein fusing the exposure-specific sets of candidate objects includes:
combining the exposure-specific sets of candidate objects; and
removing a subset of candidate objects by non maximal suppression (NMS).
8 . The method of claim 1 , wherein fusing the exposure-specific sets of candidate objects includes:
merging the exposure-specific sets of candidate objects into respective ground truth objects using a keep best loss algorithm.
9 . The method of claim 1 , wherein generating multiple raw exposure-specific images includes employing an exposure selection network to determine an exposure value for an exposure t based on an exposure value for an exposure t−1.
10 . A method of detecting objects from camera-produced images comprising:
generating multiple raw exposure-specific images for a scene;
deriving for each raw exposure-specific image a respective multi-level regional illumination distribution for use in computing respective exposure settings;
performing for the multiple raw exposure-specific images respective processes of image enhancement to produce respective processed exposure-specific images;
extracting from the processed exposure-specific images respective sets of exposure-specific features collectively constituting a superset of features;
detecting a set of candidate objects using the superset of features; and
pruning the set of candidate objects to produce a set of objects within the scene.
11 . The method of claim 10 , wherein the respective processes of image enhancement include one or more of contrast stretching, demosaicing, resizing, a power transform, color correction, threshold unsharp mask filtering, affine transform, or learned gamma correction.
12 . The method of claim 10 , wherein the respective processes of image enhancement include:
applying a first color space transform to Y, Cb, Cr color space;
executing a denoising filter in the Y, Cb, Cr color space; and
applying a second color space transform to RGB color space.
13 . The method of claim 10 , wherein extracting the respective sets of exposure-specific features includes employing a ResNet neural network to generate the respective sets of exposure-specific features.
14 . The method of claim 10 , wherein extracting the respective sets of exposure-specific features includes encoding a presence of wheels, headlights, glass texture, or metal texture within the superset of features.
15 . The method of claim 10 , wherein detecting the sets of candidate objects includes computing respective bounding boxes for the superset of features.
16 . The method of claim 10 , wherein pruning the sets of candidate objects includes removing a subset of candidate objects by non maximal suppression (NMS).
17 . The method of claim 10 , wherein pruning the sets of candidate objects includes merging the exposure-specific sets of candidate objects into respective ground truth objects using a keep best loss algorithm.
18 . The method of claim 10 , wherein pruning the sets of candidate objects includes employing a late fusion standard loss algorithm.
19 . The method of claim 10 , wherein generating multiple raw exposure-specific images includes employing an exposure selection network to determine an exposure value for an exposure t based on an exposure value for an exposure t−1.
20 . The method of claim 10 , wherein extracting respective sets of exposure-specific features comprises:
employing a region proposal network (RPN) to generate exposure-specific sets of features from the processed exposure-specific images;
pooling the exposure-specific sets of features; and
cropping a region of interest (RoI) to generate the superset of features.