Nowadays,　the　Convolutional　Neural　Network　is　successfully　applied　to　the　images　object　detection.　When　new　classes　of　object　emerges,　it　is　popular　to　adapt　the　convolutional　neural　network　based　detection　model　through　a　retraining　process　with　the　new　classes　of　samples.　Unfortunately,　the　adapted　model　can　only　detect　the　new　classes　of　objects,　but　cannot　identify　the　old　classes　of　objects,　which　is　called　catastrophic　forgetting,　also　occurring　in　incremental　classification　tasks.　Knowledge　distillation　has　achieved　good　results　in　incremental　learning　for　classification　tasks.　Due　to　the　dual　tasks　within　object　detection,　object　classification　and　location　at　the　same　time,　a　straightforward　migration　of　knowledge　distillation　method　cannot　provide　a　satisfactory　result　in　incremental　learning　for　object　detection　tasks.　Hence,　this　paper　propose　a　new　knowledge　distillation　for　incremental　object　detection,　which　introduces　a　new　object　detection　distillation　loss,　a　loss　not　only　for　classification　results　but　also　for　location　results　of　the　predicted　bounding　boxes,　not　only　for　all　final　detected　regions　of　interest　but　also　for　all　intermediate　regions　proposal.　Furthermore,　to　avoid　forgetting　learned　knowledge　from　old　datasets,　this　paper　not　only　employs　hint　learning　to　retain　the　characteristic　information　of　the　initial　model,　but　also　innovatively　uses　confidence　loss　to　extract　the　confidence　information　of　the　initial　model.　A　series　of　experiment　results　on　the　PASCAL　VOC　2007　dataset　verify　the　effectiveness　of　the　proposed　method.　©　2019　IEEE.