Machine learning (ML)-enabled approaches are considered a substantial support technique of detection and classification of obstacles of traffic participants in self-driving vehicles. Major breakthroughs have been demonstrated the past few years, even covering complete end-to-end data processing chain from sensory inputs through perception and planning to vehicle control of acceleration, breaking and steering. YOLO (you-only-look-once) is a state-of-the-art perception neural network (NN) architecture providing object detection and classification through bounding box estimations on camera images. As the NN is trained on well annotated images, in this paper we study the variations of confidence levels from the NN when tested on hand-crafted occlusion added to a test set. We compare regular pedestrian detection to upper and lower body detection. Our findings show that the two NN using only partial information perform similarly well like the NN for the full body when the full body NN's performance is 0.75 or better. Furthermore and as expected, the network, which is only trained on the lower half body is least prone to disturbances from occlusions of the upper half and vice versa.
翻译:机器辅助学习(ML)方法被认为是探测和分类自驾驶车辆交通参与者障碍的一种实质性辅助技术,过去几年已经显示出重大突破,甚至包括了从感官输入到感官输入的完整的端到端数据处理链,通过感知和规划,从加速、破碎和方向的车辆控制。 YOLO(只看一眼)是一个最先进的神经神经网络(NN)结构,通过对相机图像进行捆绑式的估测,提供物体探测和分类。由于NNE是用附加说明的图像培训的,本文中我们研究了在通过人工制作的封闭装置测试后,NNN的置信度水平与测试后添加到一个测试装置时的置信度的差异。我们比较了正常行人探测与上下体探测的比。我们的调查结果显示,在全体NNN的性能为0.75或更好时,两个仅使用部分信息的NNE的全体运行情况与N类似。此外,按照预期,这个网络仅接受过低半体的干扰最不易被上半部隔绝。