Concrete Damage Detection with CD-YOLOv8

To achieve timely and accurate detection of concrete damage

Crack, spallation, exposed bars, and corrosion

To obtain richer gradient flow information by increasing the receptive field range of each network layer

To capture the global spatial context and aggregate it into local regions to enhance features

To improve convergence speed

The deformable convolution is used to adjust the sampling locations adaptively in order to better extract complex features from the input data.

The SIoU loss function is adopted to improve convergence speed, thereby enhancing the efficiency of the detection algorithm.

The spatial context pyramid is introduced to capture the global spatial context, which is then aggregated into local regions to enhance features, ultimately aiding in the accurate detection of concrete damage.

The multiscale feature adaptive fusion module is used to obtain richer gradient flow information by increasing the receptive field range of each network layer, thereby improving the algorithm's ability to detect concrete damage.

The main types of concrete damage addressed are crack, spallation, exposed bars, and corrosion, all of which pose significant safety hazards and economic losses.