Large number of cracks, as the main disease, exist in the concrete bridge, and some cracks will be secondary dehisced after maintenance, and the crack repair traces are easily confused with concrete spalling and other defects when identifying disease intelligently, as a result of which identifying the crack repair traces accurately is not only the basis for identification of secondary cracks but also important for identification of the overall disease of concrete bridges. To obtain crack repair traces with continuous edges clearly, Poisson-noise is firstly added to the image of crack repair traces, then bilateral-filtering was adopted to smooth the added and the original noise, the Otsu algorithm was also used to segment the image of crack repair traces. The filtering effect is evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and the segmentation effect is evaluated using the running time and maximum continuous memory block (LCFB) use. The results show that the highest PSNR value of the crack repair trace images processed by the Poisson-noise and bilateral-filtering algorithm is about 35.090 1 dB, and the SSIM value reach about 0.880 1, which shows that adding Poisson-noise improves image quality and optimizes the bilateral filtering effect. The running time of image segmentation by the Otsu algorithm is about 25%-50% shorter than other methods, and meanwhile the LCFB is about 0.25% higher. The processed crack repair trace images achieve the desired effect, which verifies the effectiveness and feasibility of the method proposed.