ABSTRACT Object detection in edge computing has emerged as a pivotal solution for processing sensor data locally, thereby minimizing latency and bandwidth consumption. However, this introduces significant privacy concerns, as edge servers may exploit sensitive sensor data. Traditional frameworks often overlook data security and privacy protection, presenting challenges in balancing privacy with computational efficiency in resource‐constrained environments. To address these issues and improve performance, we propose SecuYOLO CBAMNet53, a novel framework combining Secure YOLO V3 with Darknet‐53 and the CBAM. This model enhances feature extraction capabilities, ensuring privacy protection and accurate real‐time object detection on edge devices. Darknet‐53 serves as a deep feature extractor, while CBAM boosts discriminative power through channel‐wise and spatial attention, improving detection accuracy and handling complex scenes. It integrates YOLOv3 with security measures like data encryption, secure deployment, and differential privacy, safeguarding sensitive information during inference. By classifying data as public or private for smart IoT applications, SecuYOLO CBAMNet53 optimizes performance and prevents privacy breaches. The proposed SecuYOLO, employing Darknet‐53 with CBAM as its backbone, achieves outstanding performance in both an average recall of 67.62 and an average precision of 51.2. The model's dynamic recalibration of feature maps and robust security mechanisms ensure reliable object detection and privacy protection, making it suitable for real‐world applications.