Flame target detection method, electronic equipment and storage medium

• Main Authors: ZHU TAOJIAN, SHEN CHUANGYUN, LIU BIAO, BAI LIN, SHU HAIYAN, ZENG YIFAN, SU KAI, HE PEIKAI, LEI YIHUI, WANG HENGHUA
• Format: Patent
• Language: Chinese; English
• Published: 27.05.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a flame target detection method, electronic equipment and a storage medium. The method comprises the following steps: S1, obtaining an image; s2, training a flame detection model by using a deep learning convolutional neural network to obtain a detection model yo-v5; s3, each frame of the image is detected through the detection model yo-v5, and a preliminary detection result is obtained; s4, filtering the preliminary detection result through a filtering module based on flame image features; and S5, judging that the detection target meeting the judgment condition is flame, and otherwise, judging that the detection target is misjudged. According to the invention, the false detection rate during flame target detection can be reduced. 本发明公开了一种火焰目标检测方法、电子设备、存储介质，方法包括以下步骤：S1、获得图像；S2、采用深度学习卷积神经网络进行火焰检测模型的训练，得到检测模型yolo-v5；S3、通过所述检测模型yolo-v5对每一帧所述图像进行检测，得到初步的检测结果；S4、通过基于火焰图像特征的过滤模块对所述初步的检测结果进行过滤；S5、对符合判断条件的判断检测目标为火焰，否则判断为误判。本发明能够降低火焰目标检测时的误检率。