This paper proposes a new method for defect detection of aluminum profiles to solve the problems of existing methods in accuracy and small target defect detection. Firstly, the coordinate attention mechanism (CA) is introduced into the feature extraction network to prevent information loss, thereby avoiding missed defect detection. Secondly, the adaptive weighted feature pyramid (AWFPN) is introduced to optimize the receptive field of feature maps and attention fusion, thereby improving the efficiency of defect feature capture and utilization. At the same time, the bounding box regression loss function is improved to better handle the scale change and positioning error of defects, thereby improving the detection accuracy and speeding up the convergence speed of the model. Experimental results show that the method has significantly improved the detection effect, especially in the recognition ability of defects such as dirty points and scratches.