An attention-driven image segmentation method using at least one adaptive loss weight map is provided to be used for updating HD maps required to satisfy level 4 of autonomous vehicles. By this method, vague objects such as lanes and road markers at distance may be detected more accurately. Also, this method can be usefully performed in military, where identification of friend or foe is important, by distinguishing aircraft marks or military uniforms at distance. The method includes the steps of allowing a learning device to: instruct a softmax layer to generate softmax scores; instruct a loss weight layer to generate predicted error values and apply loss weight values by applying loss weight operations to the predicted error values; and instruct a softmax loss layer to generate adjusted softmax loss values by referring to initial softmax loss values, generated by referring to the softmax scores and their corresponding GTs, and the loss weight values.
4 HD LEARNING METHOD AND LEARNING DEVICE FOR ATTENTION-DRIVEN IMAGE SEGMENTATION BY USING AT LEAST ONE ADAPTIVE LOSS WEIGHT MAP TO BE USED FOR UPDATING HD MAPS REQUIRED TO SATISFY LEVEL 4 OF AUTONOMOUS VEHICLES AND TESTING METHOD AND TESTING DEVICE USING THE SAME