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In this study, a basic CNN is used to detect accidents from images taken by surveillance cameras. The method sorts the traffic scenes either as crash cases or normal ones, reaching close to 91 percent correct classifications during testing. Data handling steps are explained along with the model structure and how it was trained, followed by performance numbers. Practical use in smart transport setups is considered; next steps include improving reliability, adding time-sequence analysis, plus running the system on low-power devices. Results show even straightforward deep learning designs may work well for spotting crashes, suggesting possible adoption in roadside units or vehicle-mounted monitors.