Design And Implementation Of Fireworks Detection Algorithm Based On Emergency Deployment Ball-control

Emergency rescue is the prevention,preparation,response and recovery activities and plans for all kinds of emergencies.All kinds of emergencies often associated with the occurrence of fire,fire is extremely destructi ve,affects a broad scope,a serious threat to human life and property security,the harm of fire to human beings is much higher than other emergencies.In order to guarantee the emergency communication during the emergency rescue,an emergency communication command system with wide and narrow band fusion has been developed at present,which lacks the fireworks detection function in the emergency deployment ball-control.The early sensor-based fireworks detector are costly and has false alarm rales.In recent years,the fireworks detection algorithm based on deep learning has greatly improved the performance of fireworks detection,but there are some problems such as low detection accuracy and poor robustness.Therefore,it is of great significance to implement an improved fireworks detection algorithm based on emergency deployment ball-control.In this paper,aiming at the problems of poor image quality and low contrast of the fireworks dataset,which uses Gaussian filtering noise reduction and Histogram equalization to preprocess fireworks images to improve the image quality of fireworks dataset.At the same time,in order to reduces the impact of static objects suspected to be fireworks on fireworks detection,the Five-frame frame difference method is adopted for dynamic target extraction,and the improved Canny edge detection algorithm is proposed to "and" fuse with the above algorithm.The algorithm can obtain clearer and more complete dynamic target contour to improve the accuracy of fireworks detection.Secondly,in view of the problems of poor generalization ability,low detection accuracy,and poor detection effect of overlapping targets of the YOLOv5 firew orks detection model.the multi-stage improvement based on the YOLOv5 fireworks detection model was realized.Firstly,Mosaic-9 data enhancement and Label smoothing were carried out at the input to improve the generalization ability of the model and prevent overfitting.And the fusion CBAM and EC A-Net attention module are added to the feature fusion layer to highlight the fireworks targets in the image,at the same time,Kmeans++clustering algorithm is used to improved the matching degree between prior boxes and target boxes,and the accuracy of fi reworks detection is improved.Finally,Soft-NMS is adopted to improve the detection effect of overlapping fireworks targets.Through experimental analysis,compared with the original YOLOv5,theaverage precision of the improved fireworks detection model is increased by 5.6%to 93.69%.Finally,the DeepStream framework is adopted to realize the deployment of the improved fireworks detection model in Jetson Nano embedded motherboard,and the voice alarm basedon DeepStream underlying framework Gstreamer is implemented.Connected with theemergency deployment ball-control,the overall function of the system is tested and analyzed,fireworks detection accuracy is greater than 93%,false positives are less than 2%,and voice alarm accuracy is 99%,which greatly improves the accuracy of fireworks detection and reduces various losses caused by fireworks disasters in emergency rescue.