Concrete roads have high durability and long-term performance. To ensure this, proper design, good quality application and necessary maintenance are essential. Maintenance/repair activities are carried out to increase the life of the roads and the volume of traffic that they can carry safely. These activities are started by first illustrating the current condition of the road superstructure at periodic intervals and identifying damaged areas. However, this determination process is very tiring, time-consuming and costly for long distances. This study focused on detecting cracks in concrete roads for various shooting, weather conditions and illumination levels using a deep learning-based object detection method. In this way, existing cracks can be determined in a remarkably short time and inexpensive. A descriptive approach is considered to detect cracks of images captured on concrete road surfaces using a pre-trained Faster R-CNN. To demonstrate the effectiveness of the proposed method, 323 images with a resolution of 4128 x 2322 pixels and an aspect ratio of 16:9 to verify the performance of damage detection on the selected concrete road surface. This experimental study produces bounding boxes of 1128 objects for the object detection process. A parametric study to verify the quality of the training level was carried out by changing the shooting conditions on the concrete road. The shooting distance and shooting height were taken as variables and crack detection analyses were carried out. The effects of the weather conditions and illumination level were also investigated. Crack detection analysis of the photographs captured on sunny, cloudy and foggy weather conditions, at sunset and moonlight times of the day, between 5:00-6:00 pm, 6:00-7:00 pm and 7:00-8:00 pm, which corresponds to dark periods of the day has been made. According to the results obtained, the number of cracks detected on a sunny day does not change on a cloudy and foggy day. It decreases by 50% for sunset and moonlight, by about 25% at 6:00-7:00 pm and by about 85% at 7:00-8:00 pm; however, it does not change at 5:00-6:00 pm.