<p>With the rapid evolution of intelligent transportation systems in next-generation networks, the efficient and accurate object detection has become essential for maintaining the highway safety and reliability. Although the centralized or cloud-based object detection provides huge computing resources, it inevitably suffers from the high latency and bandwidth overhead. On the contrast, the edge-only computing paradigm is with low latency, but has to face challenges such as the small object pixel proportion, low localization accuracy of general models and small crack semantic segmentation. To bring together advantages from both cloud and edge computing, we propose a cloud–edge collaborative two-stage object detection and semantic segmentation method. First, to tackle the weak detection by the small proportion of object pixels, a detection model based on super-resolution feature generation is developed, which processes the dataset by removing low-quality subsets and supplementing the self-made data, and then uses relevant backbone networks with other components to enhance the representation ability of small targets. Second, for the semantic segmentation of small targets, a segmentation model based on the local perception is proposed. In particular, by applying it to Mask2Former with an additional auxiliary convolution layer, the model can well capture local details and low-dimensional semantic information, improving the segmentation precision. Finally, experiments demonstrate that the proposed cloud–edge collaborative two-stage method achieves the higher detection accuracy and richer segmentation details over existing models.</p>