Fire　is　one　of　the　most　common　hazards　in　the　process　industry.　Until　today,　most　fire　alarms　have　had　very　limited　functionality.　Normally,　only　a　simple　alarm　is　triggered　without　any　specific　information　about　the　fire　circumstances　provided,　not　to　mention　fire　forecasting.　In　this　paper,　a　combined　real-time　intelligent　fire　detection　and　forecasting　approach　through　cameras　is　discussed　with　extracting　and　predicting　fire　development　characteristics.　Three　parameters　(fire　spread　position,　fire　spread　speed　and　flame　width)　are　used　to　characterize　the　fire　development.　Two　neural　networks　are　established,　i.e.,　the　Region-Convolutional　Neural　Network　(RCNN)　for　fire　characteristic　extraction　through　fire　detection　and　the　Residual　Network　(ResNet)　for　fire　forecasting.　By　designing　12　sets　of　cable　fire　experiments　with　different　fire　developing　conditions,　the　accuracies　of　fire　parameters　extraction　and　forecasting　are　evaluated.　Results　show　that　the　mean　relative　error　(MRE)　of　extraction　by　RCNN　for　the　three　parameters　are　around　4–13%,　6–20%　and　11–37%,　respectively.　Meanwhile,　the　MRE　of　forecasting　by　ResNet　for　the　three　parameters　are　around　4–13%,　11–33%　and　12–48%,　respectively.　It　confirms　that　the　proposed　approach　can　provide　a　feasible　solution　for　quantifying　fire　development　and　improve　industrial　fire　safety,　e.g.,　forecasting　the　fire　development　trends,　assessing　the　severity　of　accidents,　estimating　the　accident　losses　in　real　time　and　guiding　the　fire　fighting　and　rescue　tactics.　©　2022　The　Institution　of　Chemical　Engineers