Most sound imaging instruments are currently used as measurement tools which can provide quantitative data, however, a uniform method to directly and comprehensively evaluate the results of combining acoustic and optical images is not available. Therefore, in this study, we define a localization error index for sound imaging instruments, and propose an acoustic phase cloud map evaluation method based on an improved YOLOv4 algorithm to directly and objectively evaluate the sound source localization results of a sound imaging instrument. The evaluation method begins with the image augmentation of acoustic phase cloud maps obtained from the different tests of a sound imaging instrument to produce the dataset required for training the convolutional network. Subsequently, we combine DenseNet with existing clustering algorithms to improve the YOLOv4 algorithm to train the neural network for easier feature extraction. The trained neural network is then used to localize the target sound source and its pseudo-color map in the acoustic phase cloud map to obtain a pixel-level localization error. Finally, a standard chessboard grid is used to obtain the proportional relationship between the size of the acoustic phase cloud map and the actual physical space distance; then, the true lateral and longitudinal positioning error of sound imaging instrument can be obtained. Experimental results show that the mean average precision of the improved YOLOv4 algorithm in acoustic phase cloud map detection is 96.3%, the F1-score is 95.2%, and detection speed is up to 34.6 fps. The improved algorithm can rapidly and accurately determine the positioning error of sound imaging instrument, which can be used to analyze and evaluate the positioning performance of sound imaging instrument.

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