## 1 Introduction and Literature Review  
Particleboard serves as the primary foundation material for customized furniture. With the growing demand in the customized furniture market, the need for particleboard is also increasing [1, 2]. As production increases, the current reliance on subjective observation for detecting the appearance quality of particleboard is no longer sufficient to meet production demands [3]. It is essential to explore a more accurate, objective, and efficient detection method to replace manual detection. The integration of machine vision and deep learning has led to the development of object detection technology [4; 5; 6; 7; 8], which enables intelligent and automated assessment of the particleboard appearance quality. In the field of particleboard surface defect detection, YOLO algorithms have gained significant research attention [9; 10]. In 2021, [PERSON] et al. [11] proposed an improved YOLOv5-based object detection algorithm, PB-YOLOv5, which was capable of detecting five defect types (big shavings, sand leakage, glue spot, soft, and oil pollution) on particleboard surfaces. This study is devoted to the lightweight and real-time performance of the algorithm. The mean average precision (mAP) was improved by 2.3% compared to the original YOLOv5 algorithm. In 2023, [PERSON] et al. [12] proposed a lightweight object detection algorithm called Lite-YOLOv5s, which was based on an improved version of YOLOv5s. This algorithm effectively detected four kinds of surface defects on particleboard: shavings, glue spots, oil stains and dust spots, and core leakage. To achieve this, the study incorporated a ghost bottleneck module to reduce the model's scale of parameters and a coordinate attention (CA) module to enhance the network's capability to concentrate on the location of defects within the backbone. As a result, Lite-YOLOv5s remarkably reduced the scale of parameters by 63.5% compared to the original algorithm. However, this unilateral emphasis on lightweight architecture led to a slight decrease in mAP by 1.3%. In 2023, [PERSON] et al. [13] proposed an improved convolutional block attention module (CBAM) for the overfitting problem caused by the small samples. They applied it to capsule networks to realize the classification of five defects, and the accuracy rate was increased by 10% compared to the baseline of 95.6%. In 2024, [PERSON] et al. [14] proposed an improved YOLOv5s small defect detection algorithm, YOLOv5s-ATG, in response to the poor accuracy of the current particleboard defect detection on small objects, which improved the mAP by 4.6% compared to the original YOLOv5s algorithm. [PERSON] et al. [15] introduced the MobileViT [16] module into the YOLOv5s network, combining the CA module to optimize the detection head and the focal loss function to improve the model's capability to recognize defects with complex scale variations as well as the defects of small objects. Despite the findings of the studies mentioned, there are still several limitations to consider:  
1. Existing improvement schemes based on YOLO primarily emphasize making the model lightweight or optimizing a single scale, which is unsatisfactory for detecting multi-scale defects that co-occur. When multi-scale defects coexist within a single scene, such as large oil pollution and small shavings, conventional feature fusion methods tend to dilute the features of smaller objects within the deep network [17; 18]. This dilution can lead to missed detections. 2. The texture features of weak scratch defects are rarely detected. More attention tends to be directed toward defects with strong significance and notable grayscale, such as glue spots, shavings, and oil stains. This focus can be attributed to the shortcomings of existing methods, which lack an effective mechanism for enhancing edge features. The conventional convolutional operations within deep networks have a limited capacity to preserve the edge information [19; 20], leading to reduced confidence in detecting defects with weaker features.