Drones can provide a wider field of view, high mobility and flexibility for monitoring and analyzing traffic flows and safety conditions. In case of a perpendicular viewing angle to the ground, there will be a very less occlusion that can occur and make vehicle tracking be easier. Thus, a drone-based solution will be better for traffic conflict hotspot detection at an interaction. However, due to its observation far from the ground, limited battery time, and bandwidth, this solution should be edge-based and have a good recognition rate in small object detection. However, current edge-based SoTA (state-of-the-art) methods are weak in a small object detection. We propose CoBiF net (Concatenated Bi-Fusion feature pyramid network), a one-stage object detection model for a real-time small object detection, which consists of SPP (spatial pyramid pooling), FE (Feature Extractor), CF (Concatenated Feature) block, and BFM (Bottom-up Fusion Module). CoBiF net is memory-and-bandwidth saving for the most edge devices. Extensive experiments on UA VDT benchmark show the proposed method achieved the SoTA results for the small object detection task in terms of accuracy and efficiency.