The　object　detection　technology　holds　paramount　significance　in　realizing　autonomous　driving　and　AI-assisted　driving　systems.　Swift　and　precise　object　detection　is　crucial　for　enhancing　the　safety　of　autonomous　vehicles.　However,　for　in-vehicle　edge　computing　platforms,　colossal　models　fall　short　of　meeting　real-time　detection　requirements,　while　lightweight　models　often　compromise　on　detection　accuracy.　Addressing　this　issue,　this　paper　proposes　an　improved　real-time　object　detection　algorithm　based　on　YOLOv5.In　the　proposed　method,　we　combine　the　One-Shot　Aggregation　(OSA)　concept　with　the　progressive　channel　compression　idea　and　introduce　GSConv　to　innovatively　propose　the　GSPCA　structure.　It　aims　to　improve　some　of　the　problems　exposed　by　the　original　C3　structure,　so　as　to　enhance　the　model　efficiency.　Secondly,　we　also　apply　GSConv　to　the　neck　network　of　YOLOv5　and　introduce　the　Content-Aware　ReAssembly　of　Features　(CARAFE)　upsampling　operator　in　the　FPN　structure,　which　utilizes　its　spatial　perception　and　large　receptive　field　to　improve　the　quality　of　the　upsampling,　thus　enhancing　the　feature　fusion　performance　of　the　network.　Experimental　results　demonstrate　that,　compared　to　the　baseline,　our　proposed　model　achieves　the　highest　improvement　of　5.2%　in　mAP@0.5:0.95　on　the　PASCAL　VOC　dataset,　KITTI　dataset,　and　SODA10m　dataset.　Furthermore,　the　model＇s　parameter　count　and　computational　load　are　slightly　less　than　those　of　the　original　model.　©　2023　ACM.