Object　detection　has　achieved　significant　progress　in　attaining　high-quality　performance　without　leaking　private　messages.　However,　traditional　approaches　cannot　defend　the　poisoning　attacks.　Poisoning　attacks　can　make　the　predictive　model　unusable,　which　quickly　causes　recognition　errors　or　even　traffic　accidents.　In　this　paper,　we　propose　a　privacy-preserving　object　detection　with　poisoning　recognition　(PR-PPOD)　framework　via　distributed　training　with　the　help　of　the　CNN,　ResNet18,　and　classical　SSD　network.　Specifically,　we　design　a　poisoning　model　recognition　algorithm　to　remove　the　uploaded　local　poisoning　parameters　to　guarantee　a　trained　model＇s　availability　based　on　given　privacy-preserving　progress.　More　importantly,　the　PR-PPOD　framework　can　effectively　prevent　the　threat　of　differential　attacks　and　avoid　privacy　leakage　caused　by　reverse　model　reasoning.　Moreover,　the　effectiveness,　efficiency,　and　security　of　PR-PPOD　are　demonstrated　via　comprehensive　theoretical　analysis.　Finally,　we　simulate　the　performance　of　local　poisoning　model　recognition　based　on　the　MNIST,　CIFAR10,　VOC2007,　and　VOC2012　datasets,　which　could　achieve　good　performance　compared　with　the　case　without　poisoning　recognition.