With　the　rapid　development　of　deep　learning　techniques,　deep　convolutional　neural　networks　(DCNNs)　have　become　more　important　for　object　detection.　Compared　with　traditional　handcrafted　feature-based　methods,　the　deep　learning-based　object　detection　methods　can　learn　both　low-level　and　high-level　image　features.　The　image　features　learned　through　deep　learning　techniques　are　more　representative　than　the　handcrafted　features.　Therefore,　this　review　paper　focuses　on　the　object　detection　algorithms　based　on　deep　convolutional　neural　networks,　while　the　traditional　object　detection　algorithms　will　be　simply　introduced　as　well.　Through　the　review　and　analysis　of　deep　learning-based　object　detection　techniques　in　recent　years,　this　work　includes　the　following　parts:　backbone　networks,　loss　functions　and　training　strategies,　classical　object　detection　architectures,　complex　problems,　datasets　and　evaluation　metrics,　applications　and　future　development　directions.　We　hope　this　review　paper　will　be　helpful　for　researchers　in　the　field　of　object　detection.　©　2020,　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.