# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 1 Introduction  
With the rapid development of artificial intelligence technology, the use of robotic arms in fields such as industrial automation, healthcare, and aerospace is becoming increasingly widespread [1]. To achieve precise detection and tracking of targets by robotic arms, target tracking algorithms based on machine vision have emerged as a research hotspot. The attention mechanism originated from the study of human visual systems, allowing for the swift location of, and focus on, key information within complex environments [2, 3]. Within the sphere of target tracking by robotic arms, the attention mechanism can assist in the rapid identification of targets and locking onto them within dynamic, complex environments. This results in improved accuracy and stability of tracking [4, 5]. The active exploration of detecting and tracking small moving targets through machine vision represents a prominent research area within computer science [6]. Recent years have seen sustained interest and contributions from researchers worldwide, leading to continuous innovation and enhancement of algorithms for small moving target detection [7]. Given the intricate fluctuations present in dynamic detection environments--including variations in lighting and background noise--achieving high accuracy and robustness in dynamic target detection algorithms continues to pose significant challenges. Currently, the main approaches for small moving target detection algorithms include background elimination, optical flow, and inter-frame difference methods. Many scholars and institutions worldwide have conducted research into these small moving target detection algorithms and have yielded varying insights. [PERSON] [8] developed an image classification system that quickly categorizes a large number of factory-produced images into defective or non-defective classifications usingcomputer vision technology. The system utilizes Canny edge detection and SIFT (scale-invariant feature transform) matching algorithms for image matching. To obtain correctly classified photos, these images were compiled to create the test dataset provided to the system. Many of the enterprise's products have been covered by this program. However, the system is sensitive to variations in lighting, suggesting less environmental robustness. [PERSON] [9] presented YOLO-SG, a salience-guided (SG) deep learning model that improves small object detection by attending to detailed regions via a generated salience map. YOLO-SG performs two rounds of detection: coarse detection and salience-guided detection. In the first round of coarse detection, YOLO-SG detects objects using a deep convolutional detection model and proposes a salience map utilizing the context surrounding objects to guide the subsequent round of detection. In the second round, YOLO-SG extracts salient regions from the original input image based on the generated salience map and combines local detail with global context information to improve the object detection performance. Experimental results have demonstrated that YOLO-SG outperforms the state-of-the-art models, especially when detecting small objects. [PERSON] [10] posited that in the field of maritime navigation safety, it is important to identify whether certain specific items appear in the cockpit to help determine if there exists a threat to navigation safety. These items are generally small in size and necessitate higher detection efficiency. To address this issue, this thesis proposed a ship-specific item detection method that improved on the YOLOv5s algorithm. By introducing the CBAM (convolutional block attention module) [11], the proposed method enhanced the network's feature extraction capabilities and improved detection ability and accuracy for small targets.