Several　deep　learning　techniques　have　been　used　to　detect　pavement　cracks　for　the　partial　replacement　of　inefficient　traditional　inspections.　However,　the　extensively　varying　real-world　situations　limit　the　detection　accuracy.　While　many　existing　studies　utilized　the　attention　modules　in　pavement　crack　detection　to　improve　model　performance,　few　studies　considered　the　impact　of　＂how＂　and　＂where＂　to　add　attention　modules　on　model　performance,　namely　the　module　optimization.　Combined　with　the　attention　mechanism,　a　new　pavement　crack　detection　method　was　proposed　based　on　the　You　Only　Look　Once　5th　version　(YOLOv5)　in　this　paper.　Considering　two　adding　ways　and　three　adding　positions,　the　spatial　and　channel　squeeze　and　excitation　(SCSE)　module　and　convolutional　block　attention　module　(CBAM)　were　used　to　build　a　total　of　12　different　attention　models　for　the　cracking　detection.　Each　model　was　trained　on　3248　images,　and　the　weight　with　the　best　performance　on　the　validation　set　was　saved　for　testing.　The　test　results　show　that　the　mAP@0.5:0.95　of　the　best　attention　model　is　improved　by　nearly　6.7%　compared　to　the　original　model　without　the　attention　mechanism.　In　addition,　it　can　process　images　at　13.15ms/pic　while　maintaining　94.4%　precision,　fully　meeting　the　needs　of　real-time　detection.　Compared　with　the　existing　pavement　crack　detection　methods,　the　advantages　of　the　proposed　method　include　a　great　detection　speed,　high　accuracy,　and　good　robustness.