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For networked sensors that report binary local decisions to a fusion center, we use a fusion rule that employs the summation of these local decisions for hypothesis testing. Based on the assumption that the received signal power decays as the distance from the target increases, exact system level detection performance measures are derived analytically. The evaluation of the probability of detection involves multiple-fold integrations. Two approximations of the probability of detection, by using binomial distribution with or without ignoring the border effect of the region of interest (ROI), are presented. It is shown that for various system parameters we have explored, the approximation that takes into account the border effect provides a very accurate estimation of the probability of detection. To achieve a better system level detection performance, the local sensor level decision threshold is chosen such that it maximizes the Kullback Leibler distance of the distributions conditioned on the two hypotheses.