Locates and categorizes objects in images and video streams, including pedestrians, motor vehicles, non-motor vehicles, animals, and miscellaneous goods. Typical algorithms and models: YOLO, Faster R-CNN, SSD, RetinaNet, EfficientDet, etc. Core Capabilities： Performs simultaneous detection for multi-class, multi-scale, and multiple objects. Enables detection of tiny objects, occluded objects, and densely clustered objects. Deployment scenarios: security surveillance, traffic management, retail analytics, industrial inspection, robot perception.
It continuously localizes identical objects and sustains consistent ID association across consecutive video frames. Typical algorithms: DeepSORT, ByteTrack, StrongSORT, FairMOT, etc. Core Capabilities: Persistent ID retention across frames. Re-identification of targets after occlusion. Application Scenarios: Passenger flow counting, vehicle flow statistics, behavior analysis, security monitoring, robot following.
Semantic Segmentation: Assigns category labels to every pixel in an image (roads, buildings, sky, vegetation, human figures, etc.). Typical models: U-Net, DeepLab, SegFormer, etc. Application scenarios: Autonomous driving perception, medical image segmentation, remote sensing analysis, industrial defect region localization. Instance Segmentation: It executes category classification while differentiating distinct individual objects belonging to the same class (e.g., multiple pedestrians, multiple vehicles). Typical models: Mask R-CNN, SOLO, YOLACT, etc. Application scenarios: Robotic grasping, industrial sorting, security traffic/people counting, retail merchandise recognition. Panoptic Segmentation: Integrates semantic and instance segmentation branches: it generates instance masks for foreground objects and semantic labels for background zones. Application scenarios: Autonomous driving, urban environment perception, high-accuracy scene comprehension.
Human Pose Estimation: Detects human body keypoints (head, shoulders, elbows, wrists, hips, knees, ankles, etc.) and outputs human skeletal structures. Typical models: OpenPose, HRNet, YOLO-Pose, etc. Applications: Behavior analysis, fall detection, motion recognition, fitness and sports posture correction, human-computer interaction. Hand & Limb Keypoint Estimation: Supports 21 hand keypoints, finger pose tracking and gesture recognition. Applications: VR/AR interaction, sign language recognition, dexterous robotic manipulation, contactless gesture control.
Dedicated to the research and development of core visual algorithm technologies, product innovation and industry applications, empowering AI+ diversified scenarios, facilitating industrial upgrading
Contact Us