Achieving reliable target detection in the field of sonar imagery represents a significant challenge due to the complex underwater interference patterns characterized by speckle noise, tunnel effects, and low-signal-to-noise ratio (SNR) environments. Currently, constant false alarm rate (CFAR) detection denotes a fundamental target detection method in sonar target recognition. However, conventional CFAR methods face some limitations, including a slow computational speed, a high false alarm rate (FAR), and a notable missed detection rate (MDR). To address these limitations, this study proposes an innovative segmentation–detection framework. The proposed framework employs a global segmentation algorithm to identify regions of interest containing potential targets, which is followed by localized two-dimensional CFAR detection. This hierarchical framework can significantly improve computational efficiency while reducing the FAR, thus enabling the practical implementation of advanced, computationally intensive CFAR detection methods in real-time target detection in sonar imagery. In addition, an innovative segmented-ordered-weighting CFAR (SOW-CFAR) detection method that integrates multiple weighting windows to implement ordered weighting of reference cells is developed. This method can effectively reduce both the FAR and MDR through optimized reference cell processing. The experimental results demonstrate that the proposed method can achieve superior detection performance in sonar imagery applications compared to the existing methods. The proposed SOW-CFAR detection method can achieve fast and accurate target detection in the sonar imagery field.