# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection  
[PERSON]  
1 School of Electronic & Information Engineering, Beihang University, Beijing 100080, China 1  
[PERSON]  
2 Hefei Innovation Research Institute of Beihang University, Hefei 230012, China 2  
directional SAR ship detector with Bi-directional Feature Fusion and Angle Classification (BiFA-YOLO). Comparative experiments show that the method has better robustness and adaptability [4]. [PERSON] et al. propose a novel method for small ship detection based on the basic YOLO network structure, which achieves state-of-the-art performance [5]. [PERSON] et al. present a complete YOLO-based ship detection method using an improved YOLOv5s model, providing a practical reference for large-scale ship detection [6]. [PERSON] et al. propose a Combined Attention Augmented YOLO (CAA-YOLO) algorithm to alleviate the recognition challenges of extremely multi-scale ships due to the severe lack of texture details [7]. [PERSON] et al. propose an aerial image vehicle detection method based on the YOLO algorithm. Experiments show that the training model performs well on unknown aerial images, especially for small objects and rotating objects [8]. [PERSON] et al. utilize a YOLO-based end-to-end generic pipeline for vehicle detection without prior knowledge or additional steps in inference and achieves average recognition accuracy of 90.3% [9]. [PERSON] et al. propose a method for vehicle detection in different traffic scenarios based on an improved YOLOv5 network to reduce the false detection rate of vehicle targets [10]. [PERSON] et al. develop a unique detection method based on YOLOv3 for small objects in the Unmanned Aerial Vehicle (UAV) view. Experiments demonstrate that the performance of small object detection is significantly improved [11]. [PERSON] et al. propose an improved Residual YOLO (RES-YOLO) detection algorithm to solve the difficulties of automatic vehicle recognition. The experimental results show that the proposed algorithm can automatically recognize multiple vehicle targets and significantly reduce the missing and error rates [12]. [PERSON] et al. improve the Faster R-CNN for the bridge detection tasks of SAR images by combining a multi-resolution attention network and region-binding network [13]. [PERSON] et al. combine YOLOv4 with a point cloud algorithm to determine concrete cracks in bridges [14]. [PERSON] et al. propose a target detection algorithm BE-YOLOv5S based on YOLO, which meets the needs of bridge structure damage detection [15]. [PERSON] et al. apply the YOLOv3 algorithm to the spacecraft inspection task and improve the detection accuracy [16]. The network inference capability of the above methods is based on a desktop Graphics Processing Unit (GPU), which cannot guarantee real-time performance on embedded platforms. Therefore, these methods often need to be accelerated by FPGAs, Digital Signal Processors (DSPs), and Neural Processor Units (NPUs). [PERSON] et al. propose a deep YOLOv3 approach based on an embedded multi-object detection and tracking system [17]. [PERSON] et al. designed a UAV Thermal Infrared (TIR) object detection framework for images and videos based on a YOLO model with CNN architecture. The highest Mean of Average Precision (mAP) is 88.69% [18]. [PERSON] et al. propose a YOLO approach to solve the traffic sign classification problem on the mobile platform NVIDIA Jetson, which allows high-performance computing at low power consumption [19]. [PERSON] et al. propose a portable Advanced Driver Assistance System (ADAS) based on the YOLOv5 algorithm for real-time traffic signs, vehicles, and pedestrians detection. The system has excellent detection speed and accuracy for real-time road object detection on a mobile platform [20]. [PERSON] et al. propose a novel embedded YOLO model to obtain real-time and high-accuracy performance on embedded devices. The embedded YOLO model has only 3.53M parameters and can reach an average processing time of 155.1 FPS [21].