Focusing on the problems of the large time consumption of manual detection and the insufficient precision of the current detection methods of elongated pavement distress， a two-stage elongated pavement distress detection method， named Epd RCNN （Elongated pavement distress Region-based Convolutional Neural Network）， which could accurately locate and classify the distress was proposed according to the weak semantic characteristics and abnormal geometric properties of the distress. Firstly， for the weak semantic characteristics of elongated pavement distress， a backbone network that reused low-level features and repeatedly fused the features of different stages was proposed. Secondly， in the training process， the high-quality positive samples for network training were generated by the anchor box mechanism conforming to the geometric property distribution of the distress. Then， the distress bounding boxes were predicted on a single high-resolution feature map， and a parallel cascaded dilated convolution module was used to this feature map to improve its multi-scale feature representation ability. Finally， for different shapes of region proposals， the region proposal features conforming to the distress geometric properties were extracted by the proposal feature improvement module composed of deformable Region of Interest Pooling （RoI Pooling） and spatial attention module. Experimental results show that the proposed method has the mean Average Precision （mAP） of 0.907 on images with sufficient illumination， the mAP of 0.891 on images with illumination problems and the comprehensive mAP of 0.899， indicating that the proposed method has good detection performance and robustness to illumination.

Differentiable ARchiTecture Search （DARTS） can design neural network architectures efficiently and automatically. However， there is a performance “wide gap” between the construction method of super network and the design of derivation strategy in it. To solve the above problem， a differentiable neural architecture search algorithm with constraint in optimal search space was proposed. Firstly， the training process of the super network was analyzed by using the architecture parameters associated with the candidate operations as the quantitative indicators， and it was found that the invalid candidate operation none occupied the architecture parameter with the maximum weight in deviation architecture， which caused that architectures obtained by the algorithm had poor performance. Aiming at this problem， an optimized search space was proposed. Then， the difference between the super network of DARTS and derivation architecture was analyzed， the architecture entropy was defined based on architecture parameters， and this architecture entropy was used as the constraint of the objective function of DARTS， so as to promote the super network to narrow the difference with the derivation strategy. Finally， experiments were conducted on CIFAR-10 dataset. The experimental results show that the searched architecture by the proposed algorithm achieved 97.17% classification accuracy in these experiments， better than the comparison algorithms in accuracy， parameter quantity and search time comprehensively. The proposed algorithm is effective and improves classification accuracy of searched architecture on CIFAR-10 dataset.