In this paper, we propose the visual target sensing algorithm using the feature fusion and validate by flight tests. Sensing algorithm is divided by two parts: multi-feature detector and fusion. Multi-feature detector consists of the point-like feature, texture, and shape detector. The fusion part is implemented in the sequential update step of the Kalman filter-based tracking algorithm. This approach can detect the target robustly in the real outdoor environment that the scale of the target is changing. Furthermore, we designed the time-to-go-based longitudinal guidance controller with the direct visual servoing and performed the Open-loop and closed-loop flight tests by using the quadrotor and fixed-wing UAVs. The open-loop test is performed by using the quadrotor UAVs for validating the visual sensing algorithm; the closed-loop test is repeatedly performed by using the fixed-wing UAVs with the direct visual servoing-based guidance in the same environment. Finally, we analyze the results that approaching the target based on the circular error probability (CEP).