Fire accidents are commonplace. Effective fire damage assessment is pivotal for determining the fire's origin, validating losses, and facilitating post-disaster repairs. Addressing challenges in fire investigation, particularly those related to wall damage assessment-like slow inspection speeds, inconsistent results, and heavy reliance on individual experience, an intelligent wall fire damage assessment technique termed YOLOv5-wall damage assessment (YWDA) was proposed. YWDA was based on the you only look once v5 (YOLOv5) network. Primarily, the coordinate attention (CA) module was incorporated into the augmented feature extraction network, which enhancing the detection of wall fire damage characteristics. Additionally, Focal loss was integrated into the loss function to counter the imbalance of data samples. A residential fire damage assessment dataset was built through panoramic 3D modes of the fire investigation site and experimentally verified. The outcomes reveal that the YWDA outperforms alternative algorithms in speed, precision, model compactness, and robustness in real-world evaluations. Consequently, YWDA satisfies the efficiency, accuracy, and objectivity criteria for wall damage assessment, offering valuable technical reinforcement for contemporary fire investigations.