Construction　vehicles　play　an　important　role　on　construction　sites.　Real-time　and　accurate　monitoring　of　these　vehicles　can　help　oversee　site　activities,　improve　work　efficiency,　and　enhance　safety.　A　novel　engineering　vehicle　object　detection　algorithm　based　on　an　enhanced　YOLOv5　is　proposed　since　the　challenges　faced　in　object　detection　of　engineering　vehicles　in　remote　sensing　images　such　as　complex　backgrounds　and　low　detection　accuracy.　To　improve　the　detection　accuracy　of　the　model,　three　modules　are　introduced.　First,　the　Backbone＇s　CSP　Bottleneck　with　3　convolutions　(C3)　modules　are　replaced　with　the　CSP　Bottleneck　with　2　convolutions　(C2f)　modules　to　enhance　the　accuracy　and　robustness　of　the　improved　model.　Next,　Global　Attention　Mechanism　(GAM)　is　added　after　the　fourth　C2f　in　the　Backbone　to　precisely　capture　crucial　features.　Finally,　the　Neck＇s　fixed　convolutions　are　substituted　with　the　efficient　and　lightweight　Group　Shuffle　Convolution　(GSConv),　which　ensures　a　more　lightweight　improved　model　without　compromising　accuracy.　Experimental　results　demonstrate　that　compared　to　the　YOLOv5　and　some　other　benchmark　frameworks,　our　enhanced　algorithm　shows　improvements　in　precision,　recall　and　F1-score,　which　indicates　superior　performance　in　engineering　vehicle　detection.　©　2024　IEEE.