Safety helmet wearing is a powerful guarantee of workers’ personal safety. Aiming at the collected safety helmet wearing pictures have characteristics of high density， small pixels and difficulty to detect， a small object detection algorithm of YOLOv5 （You Only Look Once version 5） for safety helmet was proposed. Firstly， based on YOLOv5 algorithm， the bounding box regression loss function and confidence prediction loss function were optimized to improve the learning effect of the algorithm on the features of dense small objects in training. Secondly， slicing aided fine-tuning and Slicing Aided Hyper Inference （SAHI） were introduced to make the small object produce a larger pixel area by slicing the pictures input into the network， and the effect of network inference and fine-tuning was improved. In the experiments， a dataset containing dense small objects of safety helmets in the industrial scenes was used for training. The experimental results show that compared with the original YOLOv5 algorithm， the improved algorithm can increase the precision by 0.26 percentage points， the recall by 0.38 percentage points. And the mean Average Precision （mAP） of the proposed algorithm reaches 95.77%， which is improved by 0.46 to 13.27 percentage points compared to several algorithms such as the original YOLOv5 algorithm. The results verify that the introduction of slicing aided fine-tuning and SAHI improves the precision and confidence of small object detection and recognition in the dense scenes， reduces the false detection and missed detection cases， and can satisfy the requirements of safety helmet wearing detection effectively.

MapReduce is one of the popular distributed computing frameworks based on an open source cloud platform named Hadoop. However, the First-In First-Out (FIFO) scheduling algorithm of MapReduce is inefficient in resources utilization. A new tasks scheduling model based on resources matching rules was proposed and implemented. After obtaining the tasks resources requirement and remainder resources on computing nodes, the model assigned tasks to computing nodes based on resources matching degree to improve the usage efficiency of system resources. First of all, the model for MapReduce scheduling was established, the quantitative definition of resources and matching degree were given, and the corresponding calculation formulas were put forward. Second, the specific methods of resource measurement and the implementation of the algorithm were introduced. Compared with FIFO scheduling algorithm on TeraSort, GrepCount and WordCount, the experimental results show that the proposed model reduces by 22.19% in tasks completion time in the best case, and increases by 25.39% in throughput even in the worst case.

Modular programming of MapReduce greatly simplifies the implementation difficulty of distributed programming; however, its application scope is limited. In view of that MapReduce cannot be used to solve iteration algorithm, a new iteration MapReduce framework was proposed for evolutionary algorithm based on the study of MapReduce framework. The basic structure of the MapReduce was introduced, and the defects in implementing iteration algorithm were pointed out. The realization requirements and implementation of the proposed MapReduce framework were introduced, and the feasibility of abnormal mechanism was proposed and verified. At last, the new MapReduce framework was verified on Hadoop. The experimental results show that the parallel genetic algorithm based on the iteration MapReduce framework has higher speedup than that of MapReduce framework.