With　the　development　of　wireless　technology,　signals　propagating　in　space　are　easy　to　mix,　so　blind　detection　of　communication　signals　has　become　a　very　practical　and　challenging　problem.　In　this　paper,　we　propose　a　blind　detection　method　for　broadband　signals　based　on　a　weighted　bi-directional　feature　pyramid　network　(BiFPN).　The　method　can　quickly　perform　detection　and　automatic　modulation　identification　(AMC)　on　time-domain　aliased　signals　in　broadband　data.　Firstly,　the　method　performs　a　time-frequency　analysis　on　the　received　signals　and　extracts　the　normalized　time-frequency　images　and　the　corresponding　labels　by　short-time　Fourier　transform　(STFT).　Secondly,　we　build　a　target　detection　model　based　on　YOLOv5　for　time-domain　mixed　signals　in　broadband　data　and　learn　the　features　of　the　time-frequency　distribution　image　dataset　of　broadband　signals,　which　achieves　the　purpose　of　training　the　model.　The　main　improvements　of　the　algorithm　are　as　follows:　(1)　a　weighted　bi-directional　feature　pyramid　network　is　used　to　achieve　a　simple　and　fast　multi-scale　feature　fusion　approach　to　improve　the　detection　probability;　(2)　the　Efficient-Intersection　over　Union　(EIOU)　loss　function　is　introduced　to　achieve　high　accuracy　signal　detection　in　a　low　Signal-Noise　Ratio　(SNR)　environment.　Finally,　the　time-frequency　images　are　detected　by　an　improved　deep　network　model　to　complete　the　blind　detection　of　time-domain　mixed　signals.　The　simulation　results　show　that　the　method　can　effectively　detect　the　continuous　and　burst　signals　in　the　broadband　communication　signal　data　and　identify　their　modulation　types.