Although four-dimensional (4D) light field imaging has many advantages over traditional two-dimensional (2D) imaging, its high computation cost often hinders the application of this technique in many fields, such as object detection and tracking. This paper presents a hybrid method to accelerate the object detection in light field imaging by integrating the deep learning with the depth estimation algorithm. The method takes full advantage of computation imaging of the light field to generate an all-in-focus image, a series of focal stacks, and multi-view images at the same time, and convolutional neural network and defocusing are consequently used to perform initial detection of the objects in three-dimensional (3D) space. The estimated depths of the detected objects are further optimized based on multi-baseline super-resolution stereo matching while efficiency is maintained, as well by compressing the searching space of the disparity. Experimental studies are conducted to demonstrate the effectiveness of the proposed method.

Quality subgrade filler is scare in the construction of highway in the desert area, aeolian sand is selected as subgrade filler. The basic physical properties and influence law of static mechanical index of resilient modulus, CBR of aeolian sand affected by moisture content ratio, dry density, compactness are analyzed studied, Empirical formulas are derived between CBR, resilient modulus and water content ratio, compactness by mathematical statistical methods; The variation regularities of dynamic compaction characteristics are researched which are influenced by test methods, vibration time, the way of filling, water content ratio etc. simultaneously. The results can provide guidance for construction and control of highway in this region.

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