In order to improve the accuracy of crowd detection in crowded scenes， a dense crowd detection algorithm based on improved Faster Region-based Convolutional Neural Network （Faster R-CNN） was proposed. Firstly， the spatial and channel attention mechanisms were added to feature extraction stage and Strong-Bidirectional Feature Pyramid Network（S-BiFPN） was used to replace the multi-scale Feature Pyramid Network （FPN） in the original network， so that the network was able to autonomously learn important features and the extraction of deep image features was strengthened. Secondly， Multi-Instance Prediction （MIP） algorithm was introduced to predict instances， thus avoiding the model’s missed detection of targets in crowded scenes. Finally， Non-Maximum Suppression （NMS） in the model was optimized， and an additional Intersection over Union （IoU） threshold was added to accurately suppress the interference items of the detection results. Experimental results on the open source dense crowd detection dataset show that compared with the original Faster R-CNN algorithm， the proposed algorithm has the Average Precision （AP） increased by 5.6%， and Jaccard index value increased by 3.2%. The proposed algorithm has high detection precision and stability， which can meet the needs of crowd detection in dense scenes.

China's air pollution control policy has gradually shifted from pollution control to risk prevention and control, and existing air quality monitoring equipment and platform services are limited to environmental monitoring rather than exposure monitoring. Aiming at this problem, a comprehensive visual analysis and decision support platform based on B/S architecture-Air Pollution Exposure Risk Measurement System (APERMS) was designed and developed. Firstly, based on air pollution concentration monitoring data and exposure spatio-temporal behavior patterns, the complete air pollution exposure risk measurement technology route of pollution concentration mapping, individual exposure measurement, population exposure measurement and exposure risk assessment was researched and integrated. Secondly, based on the principle of high availability and reliability, the overall system architecture design, database design and functional modules design were carried out. Finally, GIS and J2EE Web technologies were utilized to complete the development of APERMS, realizing the high spatio-temporal resolution simulation of air pollution concentration distribution, accurate assessment of individual and population exposure of air pollution and comprehensive evaluation of air pollution exposure risk levels. The APERMS is mainly used in the air pollution monitoring and environmental health management industries, to provide effective technical support for risk aversion as well as pollution prevention and control.