Deep　neural　network　has　a　significant　improvement　in　accuracy　for　object　detection.　A　great　challenge　for　deep　learning　is　deployment　on　low-latency　inference　systems.　Knowledge　distillation　is　widely　used　for　the　reduced　computational　complexity　and　the　compatibility　with　embedded　hardware　devices.　In　this　paper,　we　proposal　two　adaptive　balance　distillation　methods　for　object　detection,　Positive-Feature　Balance　Distillation　and　Hard-Feature　Balance　Distillation.　Positive-Feature　Balance　Distillation　helps　to　alleviate　the　imbalance　of　the　positive　and　negative　feature　and　Hard-Feature　Balance　Distillation　is　used　for　forcing　the　student　to　focus　on　the　tiny　amounts　of　hard　feature　in　object　detection　task.　These　methods　are　used　to　improve　the　accuracy　of　student　networks　by　transferring　knowledge　from　a　complicated　teacher＇s　network　to　a　simplified　student＇s　network.　We　conduct　comprehensive　empirical　evaluation　with　different　knowledge　distillation　configurations　over　KITTI　that　is　one　of　the　most　important　automatic　driving　datasets.　The　proposed　methods　outperform　consistent　improvement　in　accuracy-speed　trade-offs　for　modern　object　detection　method.　　©　2022　IEEE.