Object detection (OD) is crucial for numerous emerging visual sensing applications. As OD models trained on unrepresentative data usually yield poor performance, collecting high-quality data in the local environment is recognized to be essential for improving model accuracy. Yet, the question of how to collect this data is currently largely overlooked; unsupported data collection tends to produce datasets with a significant proportion of redundant or uninformative data, hindering effective model training. To address this challenge, we design a real-time data importance estimation method and integrate it into BiGuide, a bi-level image data acquisition system we create for OD tasks. BiGuide assesses the importance of the captured images in real-time based on informativeness and diversity estimations and dynamically guides users in collecting useful data via image-level and object instance-level guidance. We prototype BiGuide in an edge-based architecture using commodity smartphones as mobile clients, and evaluate its performance via an IRB-approved study with 20 users. Our evaluation demonstrates that OD models trained on the data collected by BiGuide outperform models trained on the data collected by two baseline systems, achieving detection accuracy improvements of up to 33.07% and 14.57%, respectively. Over 85% of the users found BiGuide fast, helpful, and easy to understand and follow.