The　construction　industry　is　considered　one　of　the　most　hazardous　industries.　The　accidents　associated　with　construction　equipment　are　a　leading　cause　of　fatalities　in　the　U.S.,　with　one-quarter　of　all　fatalities　in　the　construction　industry　due　to　equipment-related　incidents,　including　collisions,　struck-by　events,　and　rollovers.　While　close　collaboration　among　multiple　equipment　and　humans　is　common,　conventional　collision　alert　mechanisms　for　equipment　usually　rely　on　distance　sensors　with　static　thresholds,　often　resulting　in　too　many　false　alarms,　causing　drivers＇　ignorance.　Considering　the　collaborative　operation　scenario,　this　research　proposes　and　develops　a　dynamic-threshold　alert　system　by　recognizing　hazardous　events　based　on　the　types　of　nearby　objects　with　their　orientation　or　postures　and　their　distances　to　the　system　carrier　equipment　based　on　image-based　recognition　and　Sim2Real　techniques.　Two　experiments　were　conducted,　and　the　results　show　that　the　system　successfully　reduced　a　large　number　of　false　near-collision　alarms　for　the　collaboration　scenarios.　Although　the　accuracy　of　object　recognition　and　image-based　distance　estimation　is　feasible　for　practical　use,　it　is　also　easily　degraded　in　the　self-obstruction　scenario　or　for　equipment　with　large　and　movable　parts　due　to　incorrect　recognition　of　the　bounding　boxes　of　the　target　objects.