Collision detection technology can reduce the probability of equipment damage and personal injury and plays an important role in modern human-robot collaborative production. To realize the collision detection without external torque sensor, it is necessary to accurately estimate the external torque of industrial robots. However, the accuracy of external torque estimation can be affected by parameters identification error of dynamic model and measurement error of motor current. To solve these problems, this paper designed a disturbance Kalman filter external torque observer based on the disturbance principle. The observer takes the equivalent external torque of external collision as the disturbance term, defines the joint disturbance model, and introduces the generalized momentum of the robot to construct the state-space equation. Considering the parameters identification error of the dynamic model and the measurement error of the motor current, it carried out an iterative estimation based on Kalman filter algorithm to obtain the optimal external torque. In order to improve the sensitivity of collision detection, a time-varying symmetric threshold function which varies with joint velocity was proposed for collision detection. The proposed method can adjust the threshold according to the change of joint velocity to adapt to the observed values of external torques at different working speeds. Experimental results show that compared with the generalized momentum observer, the accuracy of external torque estimation of the proposed observer is improved by 52.03%. In order to verify the effectiveness of the proposed method, this paper used a 6-DOF series joint industrial robot to conduct collision detection experiments. The experimental results show that compared with the static threshold, the time-varying threshold method reduces the detection delay by 58.06%, which can improve the sensitivity of collision detection and is more conducive to the safe operation and collision protection of industrial robots.