Artificial　Intelligence　(AI)-based　Internet　of　Things　(IoT)　applications　benefit　greatly　from　advanced　deep　learning　models.　However,　the　increasing　complexity　and　resource　requirements　of　deep　learning　models　pose　challenges　to　computational　efficiency　and　deployment　on　resource-constrained　devices.　To　address　these　challenges,　the　paper　proposes　a　merging　technique　to　efficiently　merge　multiple　single-task　deep　learning　models　into　a　unified　multi-task　model.　The　proposed　method　merges　models　in　a　serial　fashion,　potentially　replacing　various　layers　with　a　smaller　deep　neural　sub-network　to　connect　the　layers　of　the　previous　model　and　the　latter　model＇s　layers.　This　method　improves　computational　efficiency,　and　can　create　larger　models　to　perform　the　functions　of　the　original　models　with　minimal　training　overhead.　The　paper　designs　and　implements　a　merging　model　that　supports　end-to-end　compressed　sensing　(CS)　sampling　and　object　detection,　and　the　experimental　results　verify　the　efficacy　of　the　proposed　method　in　generating　fine-tuned　multi-task　deep　neural　models　with　minimal　training　time　and　resource　costs,　making　it　a　cost-efficient　solution　for　edge-cloud　collaborative　inference.　©　2024　SPIE.