Detecting changes in a landscape is a critical issue for several evolutionary dynamic optimization techniques. This is because most of these techniques have steps to be taken as a response to the environmental changes. It may not be feasible for most of the real world problems to know a priori when a change occurs; therefore, explicit schemes should be proposed to detect the points in time when a change occurs. Although there are both sensor-based and population-based detection schemes presented in the literature for single objective dynamic optimization problems, there are no such efforts for the dynamic multi-objective optimization problems (DMOPs). This paper proposes a set of novel sensor-based change detection schemes for DMOPs. An empirical study is presented for validating the performance of the proposed detection schemes by using eight test problems which have different types and characteristics. Additionally, the proposed change detection schemes are incorporated into a dynamic multi-objective evolutionary algorithm to validate the effectiveness of the proposed change detection schemes.