While　penetration　occurs　underneath　the　workpiece,　the　raw　information　used　to　detect　it　during　welding　must　be　measurable　to　a　sensor　attached　to　the　torch.　Challenges　are　apparent　because　it　is　difficult　to　find　such　measurable　raw　information　that　fundamentally　correlates　with　the　phenomena　occurring　underneath.　Additional　challenges　arise　because　the　welding　process　is　extremely　complex　such　that　analytically　correlating　any　raw　information　to　the　underneath　phenomena　is　practically　impossible;　therefore,　handcrafted　methods　to　propose　features　from　raw　information　are　human　dependent　and　labor　extensive.　In　this　paper,　the　profile　of　the　weld　pool　surface　was　proposed　as　the　raw　information.　An　innovative　method　was　proposed　to　acquire　it　by　projecting　a　single　laser　stripe　on　the　weld　pool　surface　transversely　and　intercepting　its　reflection　from　the　mirror-like　weld　pool　surface.　To　minimize　human　intervention,　which　can　affect　success,　a　deep-learning-based　method　was　proposed　to　automatically　recognize　features　from　the　single-stripe　active　vision　images　by　fitting　a　convolutional　neural　network　(CNN).　To　train　the　CNN,　spot　gas　tungsten　arc　welding　experiments　were　designed　and　conducted　to　collect　the　active　vision　images　in　pairs　with　their　actual　penetration　states　measured　by　a　camera　that　views　the　backside　surface　of　the　workpiece.　The　CNN　architecture　was　optimized　by　trying　different　hyperparameters,　including　kernel　number,　kernel　size,　and　node　number.　The　accuracy　of　the　optimized　model　is　about　98%　and　the　cycle　time　in　the　personal　computer　is　similar　to　0.1　s,　which　fully　meets　the　required　engineering　application.