[Case Study] Automotive Image Recognition Control System
Improving the safety and response speed of autonomous vehicles through high-precision object detection and situational awareness!
We would like to introduce a case study of the development of an "Automotive Image Recognition Control System" at our company. During the development, we faced challenges such as ensuring the versatility of image processing that is robust against adverse conditions like low light, backlighting, and raindrops. To overcome these challenges, we are implementing multi-sensor fusion with infrared cameras, distillation of deep learning models, and support for compact GPUs. 【Technical Challenges】 ■ Ensuring the versatility of image processing that is robust against adverse conditions like low light, backlighting, and raindrops ■ Maintaining a balance with onboard processing capabilities through model lightweighting ■ Optimizing the end-to-end pipeline to minimize detection latency *For more details, please download the PDF or feel free to contact us.
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【Main Achievements】 ■Pedestrian and obstacle recognition accuracy: 30% reduction in false recognition rate ■Frame processing speed: 1.5 times faster ■Complex scene identification: Stable performance maintenance ■Real-time warning response: Suppression of false detections *For more details, please download the PDF or feel free to contact us.
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For more details, please download the PDF or feel free to contact us.
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Our company acts based on a sense of "mission" as "IT professionals," focusing on the "thoughts" of each individual customer to lead them to success. We contribute to stable energy supply through energy solutions utilizing AI and IoT. Additionally, we provide services globally across our entire company group, including overseas locations. Please feel free to contact us if you have any requests.