In　cities,　a　large　amount　of　municipal　solid　waste　has　impacted　on　the　ecological　environment　significantly.　Automatic　and　robust　waste　detection　and　classification　is　a　promising　and　challenging　problem　in　urban　solid　waste　disposal.　The　performance　of　the　classical　detection　and　classification　method　is　degraded　by　some　factors,　such　as　various　occlusion　and　scale　differences.　To　enhance　the　detection　model　robustness　to　occlusion　and　small　items,　we　proposed　a　robust　waste　detection　method　based　on　a　cascade　adversarial　spatial　dropout　detection　network　(Cascade　ASDDN).　The　hard　examples　with　occlusion　in　pyramid　feature　space　are　generated　and　used　to　adversarial　training　a　detection　network.　Hard　samples　are　generated　by　the　spatial　dropout　module　with　Gradient-weighted　Class　Activation　Mapping.　The　experiment　verifies　the　effectiveness　of　our　method　on　the　2020　Haihua　AI　challenge　waste　classification.