The invention discloses a target detection method based on an evolutionary neural network under a constraint condition, and the method comprises the steps: constructing a plurality of structural blocks and a
population composed of a plurality of individuals, and carrying out the coding of each individual through a variable-length coding mode, thereby completing the initialization of the
population; performing training updating on each individual according to the training
data set; evaluating the individuals on the
verification data set, and calculating the accuracy and complexity of the individuals to obtain the fitness of the individuals; according to a preset constraint quantity, adjusting the individual fitness by using a
constraint control method, and adjusting the individual framework of which the accuracy exceeds a threshold value; selecting male parents from the
population according to the adjusted fitness, generating first-level
offspring through male parent crossing, and generating second-level
offspring through probabilistic variation of the first-level
offspring; and selecting the parent, the first-level filial generation and the second-level filial generation to generate a
new population, and performing iterative evolution. According to the invention, a light-weight structure unit is designed, a constraint method is utilized, and an optimized target detection result is achieved without artificial experience.