Cross-domain　object　detection　is　challenging　because　object　detection　models　are　significantly　susceptible　to　domain　style.　As　a　popular　semi-supervised　learning　method,　the　teacher-student　framework　(pseudo　labels　from　the　teacher　model　supervise　the　student　model)　achieves　significant　accuracy　gains　in　cross-domain　object　detection.　However,　it　suffers　from　the　domain　shift　and　prone　to　generate　low-quality　pseudo　labels,　which　limits　the　performance.　To　mitigate　this　problem,　we　propose　a　teacher-student　framework　that　utilizes　style　transfer　method,　augmentation　strategies,　and　adversarial　learning　to　address　domain　shift.　Specifically,　we　design　a　Fourier　style　transfer　method　to　reduce　the　gap　between　source　and　target　domainswithout　altering　the　semantic　information　of　the　objects.　Furthermore,　we　improve　the　data　augmentation　strategy,　by　weakly　augmenting　the　images　from　the　target　domain,　to　avoid　the　teacher　model　biased　to　the　source　domain.　Finally,　we　employ　feature-level　adversarial　training　in　the　student　model　which　is　trained　based　on　images　from　all　domains,　allowing　features　derived　from　all　domains　to　share　similar　distributions.　This　process　ensures　that　the　student　model　produces　domain-invariant　features.　Our　approach　achieves　state-of-the-art　performance　in　several　benchmark　tests.　For　example,　it　achieved　51.6%　and　49.9%　mAP　on　Foggy　Cityscapes　and　Clipart1K,　respectively.