Learning method and learning device for segmenting an image having one or more lanes by using embedding loss to support collaboration with HD maps required to satisfy level 4 of autonomous vehicles and softmax loss, and testing method and testing device using the same

A testing method for segmenting a test image having one or more lanes, comprising steps of:

• (a) on condition that a learning device (1) has instructed a convolutional neural network (CNN) module to apply at least one convolution operation to a training image to thereby generate a feature map for training and then apply at least one deconvolution operation to the feature map for training to thereby generate each of segmentation scores for training of each of pixels on the training image;
• (2) has instructed the CNN module to apply at least one Softmax operation to each of the segmentation scores for training to thereby generate each of Softmax scores for training;
• and (3) has instructed the CNN module to (I) (i) apply at least one multinomial logistic loss operation to each of the Softmax scores for training to thereby generate each of Softmax losses and (ii) apply at least one pixel embedding operation to each of the Softmax scores for training to thereby generate each of embedding losses which causes a learning of the CNN module to increase each of inter-lane differences among respective averages of the segmentation scores for training of the respective lanes and decrease each of intra-lane variances among the segmentation scores for training of the respective lanes, and then (II) learn at least one parameter of the CNN module through backpropagation by using each of the Softmax losses and each of the embedding losses, a testing device, if the test image is acquired, instructing the CNN module to apply the convolution operation to the test image to thereby generate a feature map for testing and then apply the deconvolution operation to the feature map for testing to thereby generate each of segmentation scores for testing of each of pixels on the test image;
• and
• (b) the testing device instructing the CNN module to apply the Softmax operation to each of the segmentation scores for testing to thereby generate each of Softmax scores for testing.

A learning device for segmenting an input image having one or more lanes, comprising:

• at least one memory that stores instructions;
• and
• at least one processor configured to execute the instructions to:
• perform processes of (I) instructing a convolutional neural network (CNN) module to apply at least one convolution operation to the input image to thereby generate a feature map and then apply at least one deconvolution operation to the feature map to thereby generate each of segmentation scores of each of pixels on the input image, (II) instructing the CNN module to apply at least one Softmax operation to each of the segmentation scores to thereby generate each of Softmax scores, and (III) instructing the CNN module to (1) (i) apply at least one multinomial logistic loss operation to each of the Softmax scores to thereby generate each of Softmax losses and (ii) apply at least one pixel embedding operation to each of the Softmax scores to thereby generate each of embedding losses which causes a learning of the CNN module to increase each of inter-lane differences among respective averages of the segmentation scores of the respective lanes and decrease each of intra-lane variances among the segmentation scores of the respective lanes, and then (2) learn at least one parameter of the CNN module through backpropagation by using each of the Softmax losses and each of the embedding losses.

A testing method for segmenting a test image having one or more lanes, comprising:

• at least one memory that stores instructions;
• and
• at least one processor, on condition that a learning device (1) has instructed a convolutional neural network (CNN) module to apply at least one convolution operation to a training image to thereby generate a feature map for training and then apply at least one deconvolution operation to the feature map for training to thereby generate each of segmentation scores for training of each of pixels on the training image, (2) has instructed the CNN module to apply at least one Softmax operation to each of the segmentation scores for training to thereby generate each of Softmax scores for training, and (3) has instructed the CNN module to (3-1) (i) apply at least one multinomial logistic loss operation to each of the Softmax scores for training to thereby generate each of Softmax losses and (ii) apply at least one pixel embedding operation to each of the Softmax scores for training to thereby generate each of embedding losses which causes a learning of the CNN module to increase each of inter-lane differences among respective averages of the segmentation scores for training of the respective lanes and decrease each of intra-lane variances among the segmentation scores for training of the respective lanes, and then (3-2) learn at least one parameter of the CNN module through backpropagation by using each of the Softmax losses and each of the embedding losses;
• configured to execute the instructions to:
• perform processes of (I) instructing the CNN module to apply the convolution operation to the test image to thereby generate a feature map for testing and then apply the deconvolution operation to the feature map for testing to thereby generate each of segmentation scores for testing of each of pixels on the test image, and (II) instructing the CNN module to apply the Softmax operation to each of the segmentation scores for testing to thereby generate each of Softmax scores for testing.