AI钓鱼为啥时灵时不灵？

Core Insights - The emergence of AI fishing technology has sparked interest among fishing enthusiasts, with mixed reviews regarding its effectiveness and accuracy [1][2] - Users have reported both successful and unsuccessful experiences with AI-assisted fishing, highlighting the technology's limitations in accurately detecting fish bites [1] Group 1: User Experiences - Multiple fishing enthusiasts have shared AI fishing videos on social media, showcasing the technology's ability to alert users when a fish is hooked [1] - One user, Mr. Wu from Anhui, noted that while he has caught fish using AI, he often shares videos of unsuccessful attempts, indicating a preference for entertainment over results [1] - Another user, Mr. Wu from Chongqing, expressed skepticism about the technology, suggesting it is primarily for fun and lacks maturity in its recognition capabilities [1] Group 2: Technical Limitations - Experts have identified several reasons for AI's inaccurate detection of fish bites, including challenges in recognizing floating indicators due to environmental factors [1] - High-gloss reflections on water surfaces can blind AI systems, causing them to lose track of the float's features, leading to misinterpretation [1] - Water surface ripples can create visual noise that confuses AI, resulting in false alerts when the ripple frequency overlaps with the actual fish bite signals [1] Group 3: Improvement Suggestions - To enhance the accuracy of AI fishing technology, experts recommend improving model training in low-light conditions and increasing inference speed to reduce latency and improve efficiency [2]