Early　warning　mechanism　is　crucial　for　maintaining　the　security　and　reliability　of　the　power　grid　system.　It　remains　to　be　a　difficult　task　in　a　smart　grid　system　due　to　complex　environments　in　practice.　In　this　paper,　by　considering　the　lack　of　vision-based　datasets　and　models　for　early　warning　classification,　we　constructed　a　large-scale　image　dataset,　namely　EWSPG1.0,　which　contains　12,113　images　annotated　with　five　levels　of　early　warnings.　Moreover,　104,448　object　instances　with　respect　to　ten　categories　of　high-risk　objects　and　power　gird　infrastructure　were　annotated　with　labels,　bounding　boxes　and　polygon　masks.　On　the　other　hand,　we　proposed　a　local-to-global　perception　framework　for　arly　warning　classification,　namely　EWNet.　Specifically,　a　local　patch　responsor　is　trained　by　using　image　patches　extracted　from　the　training　set　according　to　the　labeled　bounding　box　information　of　objects.　The　capability　of　recognizing　high-risk　objects　and　power　grid　infrastructure　is　transferred　by　loading　the　trained　local　patch　responsor　with　frozen　weights.　Features　are　then　fed　into　a　feature　integration　module　and　a　global　classification　module　for　early　warning　classification　of　an　entire　image.　In　order　to　evaluate　the　proposed　framework,　we　benchmarked　the　proposed　framework　on　our　constructed　dataset　with　11　state-of-the-art　deep　convolutional　neural　networks　(CNNs)-based　classification　models.　Experimental　results　exhibit　the　effectiveness　of　our　proposed　method　in　terms　of　Top-1　classification　accuracy.　They　also　indicate　that　vision-based　early　warning　classification　remains　challengeable　under　power　grid　surveillance　and　needs　further　study　in　future　work.　©　2021　Elsevier　B.V.