Recently,　convolutional　neural　networks　(CNN)　have　been　widely　used　in　object　detection　and　image　recognition　for　their　effectiveness.　Many　highly　accurate　classification　models　based　on　CNN　have　been　developed　for　various　machine-learning　applications,　but　they　generally　computationally　costly　and　require　a　hardware-based　platform　with　super　computing　power　and　memory　resources　to　implement　the　algorithm.　In　order　to　accurately　and　efficiently　achieve　object　detection　tasks　using　CNN　on　a　system　with　limited　resources　such　as　a　mobile　device,　we　propose　an　innovative　type　of　DenseNet,　which　is　a　lightweight　convolutional　neural　network　algorithm　called　Lite　Asymmetric　DenseNet　(LADenseNet).　Aiming　to　compress　the　CNN　model　complexity,　we　replace　the　7　7　convolution　and　3　3　max-pool　with　multiple　3　3　convolutions　and　a　2　2　max-pool　in　the　initial　down-sampling　process　to　significantly　reduce　the　computing　cost.　In　the　design　of　the　dense　blocks,　channel　splitting　and　channel　shuffling　are　employed　to　enhance　the　information　exchange　of　feature　maps　and　improve　the　expressive　ability　of　the　network.　We　decompose　the　3　3　convolution　in　the　dense　block　into　a　combination　of　3　1　and　1　3　convolutions,　which　can　speed　up　the　computations　and　extract　more　spatial　features　by　using　asymmetric　convolutions.　To　evaluate　the　performance　of　the　proposed　approach　we　develop　an　experimental　system　in　which　LA-DenseNet　is　used　to　extract　features　and　Single　Shot　MultiBox　Detector　(SSD)　is　used　to　detect　objects.　With　VOC2007+12　as　training　and　testing　datasets,　our　model　achieves　comparable　detection　accuracy　as　YOLOv2　with　a　fraction　of　its　computational　cost　and　memory　usage.