if self.range_weight > 0: range_loss = self.crit_ce(lane_range, kwargs['gt_ranges']) RIM模块:主要用来解决交叉车道线。对于交叉情况,一个起始点生成多份params (LSTM)。
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CondLaneNet:基于条件卷积的自顶向下车道检测框架_3D视觉工坊的博客-CSDN博客 小雷神:CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution 摘要 难点:复杂拓扑的车道线 框架:自顶向下的车道检测框架,首先检测车道实例,然后动态预测每个实例的线形状。 检测车道线实例:一种基于条件卷...
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Modern deep-learning-based lane detection methods are successful in most scenarios but struggling for lane lines with complex topologies. In this work, we propose CondLaneNet, a novel top-to-down lane detection framework that detects the lane instances first and then dynamically predicts the line ...
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在这项工作中,作者提出了CondLaneNet,一个新的自顶向下的车道检测框架,首先检测车道实例,然后动态预测每个实例的线形状。针对车道实例级识别问题,提出了一种基于条件卷积和逐行公式的条件车道检测策略。此外,作者设计了循环实例模块(RIM),以克服复杂拓扑的车道线检测问题,如密集线和分叉线。由于端到端管道的后处理较...