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Appearance detection involves accurate and efficient recognition and localization of objects in images or videos. To address the issue of detecting small-sized objects on object surfaces, this research improves the YOLOv3 network model by introducing multi-scale detection and depthwise separable convolution techniques to enhance detection accuracy and model efficiency. The deep separable convolution technique is employed to reduce computational complexity and improve model training effectiveness. Experimental results demonstrate a significant improvement in detecting small-sized objects, with an average precision of 71.52%, which is an 11.37% increase compared to the original model. Moreover, the improved algorithm achieves a detection speed of 35.6 frames per second by reducing computational overhead and enhancing model inference speed. This research has the potential to optimize the algorithm and enhance the accuracy and robustness of small-sized object detection, thereby promoting its wide application in the field of computer vision.